A method for reconstructing production operation graph model based on graph grammar

Abstract

The present invention discloses a method for reconstructing a production operation graph model based on graph grammar. First, the graph grammar is used to perform reduction operations. During the reduction process, errors in the graph are detected and different treatment measures are selected according to the error type: unrepairable unexpected errors are fed back to manual work, and repairable errors are eliminated through reduction; secondly, after the reduction operation, the reconstructed production operation graph is derived and generated based on the reverse process of the reduction from the main graph after debugging. The whole process has a low dependence on human and data resources and is automatically executed by a computer, and provides countermeasures for interruption recovery of the production operation system through graph reconstruction.

Description

Technical Field

[0001] The present invention belongs to the technical field of graph model reconstruction, and in particular to a production operation graph model reconstruction method based on graph grammar. Background Art

[0002] Graph reconstruction is a process of optimizing and reconstructing the substructure and semantic information in the graph model according to specific standards, providing effective technical support for the graph model management of complex systems, thereby improving system efficiency and reducing interruption risks and management costs. Therefore, applying graph reconstruction technology to production operation graphs can be an effective way to improve enterprise operational efficiency and risk response capabilities. Appropriate reconstruction algorithms can not only detect interruption nodes in production operation graphs to provide users with early warnings, but also provide users with appropriate interruption handling measures. In general, production operation graph reconstruction has the following two goals:

[0003] (1) Error detection: Supply interruptions in the production operation graph are usually caused by internal interruption nodes. Therefore, the premise of graph reconstruction is to detect the interruption nodes in the graph.

[0004] (2) Network reconstruction: Execute the graph reconstruction process for the errors found, and eliminate the structural factors that affect production operations through graph operations such as node deletion and replacement.

[0005] The reconstruction of the production operation diagram involves multiple disciplines such as computer science, graph theory, set theory, and management science. Currently, it can be divided into the following two categories:

[0006] (1) Re-evaluate the value of the enterprise's operating process based on business process improvement theory, and reconstruct the operating business process by deleting, merging, and replacing relevant production links;

[0007] (2) Based on statistical machine learning methods, a model is obtained through training with a large amount of data to perform operations such as link prediction and risk assessment on the enterprise production network, thereby mining the node structure and semantic associations in the production operation diagram and improving the transparency of the production operation network. It can also be used on the basis of generative machine learning to train the model using operational process data to mine the dependencies and business logic between production activities, thereby realizing the reconstruction of the production operation diagram.

[0008] However, both methods have some disadvantages, such as:

[0009] High application cost: The reconstruction based on business process improvement theory requires professionals to evaluate the enterprise operation process, which consumes a lot of manpower and material resources and lacks computing technology support. The reconstruction method using machine learning requires a large amount of industrial chain data that can be used for training. These data usually involve commercial secrets and are highly opaque, making it difficult to obtain relevant data sets.

[0010] Interruption recovery problem: The goal of reconstructing the production operation graph based on business process improvement theory is to optimize the enterprise production process and thus improve production and operation efficiency; the reconstruction method using machine learning aims to improve the transparency and robustness of the production operation network. Although both can improve the overall risk resistance of the production operation system to a certain extent, they both lack automated response measures for node interruptions, resulting in interruption recovery being overly dependent on human intervention by the risk control department, which greatly increases operating costs. Summary of the invention

[0011] The purpose of the present invention is to address the problems existing in the above-mentioned prior art and to propose a logical production operation graph reconstruction technology under the framework of graph grammar. The graph model is reconstructed by detecting, replacing and eliminating erroneous nodes in the graph model, thereby ensuring the normal development of production operations.

[0012] The technical solution to achieve the purpose of the present invention is: a method for reconstructing a production operation graph model based on graph grammar, the method comprising:

[0013] Step 1: Build a set of production rules to describe the structural model, semantic specifications, and debugging scope of the production operation graph model;

[0014] Step 2: Take the production operation graph model as input, use the production rules constructed in step 1 to perform reduction operations on the main graph, detect different types of errors and handle them separately;

[0015] Step 3, deduce the main graph processed in step 2 according to the reverse process of the reduction operation recorded in the handle stack, pop out the production rules in the handle stack and the embedded graph pointers in the reduction, i.e., the handle pointers, one by one, and use the right graph of the production rules to replace the handle to generate the reconstructed production operation graph.

[0016] Furthermore, step 1 constructs a set of production formulas to describe the structural mode, semantic specification and debugging scope of the production operation graph model, specifically including:

[0017] Step 1-1, construct terminal production rules and non-terminal production rules;

[0018] The right ends of the terminal productions are all terminal nodes, which are used to provide prerequisites for graph conversion operations;

[0019] There is a non-terminal node on the right side of the non-terminal production, which is used to determine whether the production operation graph can be reduced to the initial graph, so as to detect whether there is an unexpected error in the graph;

[0020] Step 1-2, constructing a reduction-only production; the reduction-only production defines the scope of the debuggable structure and is used to automatically eliminate repairable errors.

[0021] Furthermore, the right end of the reduction-only production is a subgraph with a repairable error, and the left end is a new subgraph reconstructed after eliminating the error.

[0022] Furthermore, in step 2, the production operation graph model is used as input, and the production formula constructed in step 1 is used to perform reduction operations on the main graph, and different types of errors are detected and processed separately, including:

[0023] Step 2-1, search the main graph for the right graph handle of the reduction production and the terminal production. If the handle is found, replace the handle with the left graph of the production and execute step 2-2, otherwise execute step 2-3;

[0024] Step 2-2, determine whether the used production is a reduction-only production. If so, it means that there is a repairable error in the main graph, and execute step 2-1; otherwise, push the used production and embedded graph pointer into the handle stack and execute step 2-1;

[0025] Step 2-3, if the current main image does not contain any repairable errors or all the repairable errors have been corrected, save the current main image;

[0026] Step 2-4, determine whether the current main image is the initial image, if so, execute step 3, otherwise execute step 2-5;

[0027] Step 2-5, search for the handles of the non-terminal production and the right graph of the terminal production in the main graph. If the handle is found, replace the handle with the left graph of the production and execute step 2-4. Otherwise, it indicates that the input graph is illegal, prompting an unexpected error, and the process stops.

[0028] Furthermore, step 3 specifically includes:

[0029] Step 3-1, determine whether the reduction-only production rule has been used in step 2. If so, execute step 3-2. Otherwise, it prompts that there is no error in the input graph and the process ends.

[0030] Step 3-2, starting from the main graph saved in step 2-3, determine whether there is a non-terminal node in the current main graph. If so, pop a production rule and the embedded graph pointer in the reduction from the handle stack, use the right end of the production rule to replace the handle indicated by the embedded graph pointer in the reduction, and execute step 3-2; otherwise, return to the current reconstructed main graph and the process ends.

[0031] The method further comprises executing, before step 2:

[0032] The production operation graph model is preprocessed, and the nodes in the production operation graph model are abstracted into entities, relationships, attributes and error nodes respectively.

[0033] On the other hand, a production operation graph model reconstruction system based on graph grammar is provided, the system comprising:

[0034] The first module is used to build a set of production-based descriptions of the structural model, semantic specifications, and debugging scope of the production operation graph model;

[0035] The second module is used to take the production operation graph model as input, perform reduction operations on the main graph using the production rules built in the first module, detect different types of errors and handle them separately;

[0036] The third module is used to deduce the main graph processed by the second module according to the reverse process of the reduction operation recorded in the handle stack, pop out the production rules in the handle stack and the embedded graph pointers in the reduction, namely the handle pointers, one by one, and use the production rule right graph to replace the handle to generate the reconstructed production operation graph.

[0037] On the other hand, a computer device is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the method is implemented when the processor executes the computer program.

[0038] On the other hand, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the method for reconstructing a production operation graph model based on graph grammar is implemented.

[0039] Compared with the prior art, the present invention has the following significant advantages:

[0040] (1) Enterprise application cost: Based on the graph grammar, the error detection and interruption recovery tasks targeted by the present invention are implemented through the reduction and deduction operations of the graph grammar, which effectively improves the standardization and automation of production operation graph detection and reconstruction. Compared with traditional methods, the present invention reduces the manpower and economic costs of detecting and reconstructing graph models based on business process improvement theory, and to a certain extent lowers the application threshold of enterprises.

[0041] (2) Detection and reconstruction scenarios: The present invention uses the reduction operation of graph grammar to detect and correct errors in various graph models involved in production operations. The process has no restrictions on the specific scenarios of detection and reconstruction. Compared with the method of using machine learning to reconstruct production operation graphs, the method of the present invention can be used for both detection and early warning before an interruption occurs and for reconstruction and recovery after an interruption occurs, providing full-process management and flowchart model optimization for enterprise production operations.

[0042] (3) Data privacy issues: The present invention performs error detection and interruption recovery on the enterprise's own production and operation network. The detection and reconstruction are based on the structural mode, semantic specifications and debugging scope of the production and operation graph model. Compared with the reconstruction method based on machine learning, the method of the present invention avoids the large-scale collection of enterprise privacy data and has high versatility, accuracy and standardization during use.

[0043] The present invention is further described in detail below in conjunction with the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 This is a flow chart of the production operation graph model reconstruction method based on graph grammar of the present invention.

[0045] Figure 2 It is a schematic diagram of an entity relationship diagram model of a production operation diagram in an embodiment.

[0046] Figure 3 A schematic diagram of a set of production rules of a debugging graph grammar in one embodiment.

[0047] Figure 4 A schematic diagram of the preprocessing process of a production operation graph model in an embodiment.

[0048] Figure 5 and Figure 6 They are two stages in the reduction process of a production operation graph in an embodiment. The first reduction stage uses terminal productions and reduction-only productions to eliminate repairable errors in the graph; the second reduction stage uses terminal productions and non-terminal productions to detect whether there are unexpected errors in the graph that require manual intervention.

[0049] Figure 7 The figure is a schematic diagram of the derivation process of the production operation diagram in an embodiment. DETAILED DESCRIPTION

[0050] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0051] It should be noted that if there are descriptions involving "first", "second", etc. in the embodiments of the present invention, the descriptions of "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or implying their relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In addition, the technical solutions between the various embodiments can be combined with each other, but they must be based on the ability of ordinary technicians in the field to implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such a combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0052] The present invention proposes a logical production operation diagram reconstruction technology under the framework of graph grammar. For the input production operation diagram model, the graph grammar is first used to perform a reduction operation on it. During the reduction process, errors in the diagram are detected and different processing measures are selected according to the error type: unrepairable unexpected errors will be fed back to humans, and repairable errors will be eliminated through reduction; secondly, after the reduction operation, the reconstructed production operation diagram is derived and generated based on the reverse process of the reduction starting from the main diagram after debugging.

[0053] In one embodiment, in combination Figure 1 , provides a method for reconstructing a production operation graph model based on graph grammar, the method comprising:

[0054] Step 1: Build a set of production rules to describe the structural model, semantic specifications, and debugging scope of the production operation graph model;

[0055] Step 2: Take the production operation graph model as input, use the production rules constructed in step 1 to perform reduction operations on the main graph, detect different types of errors and handle them separately;

[0056] Step 3, deduce the main graph processed in step 2 according to the reverse process of the reduction operation recorded in the handle stack, pop out the production rules in the handle stack and the embedded graph pointers in the reduction, i.e., the handle pointers, one by one, and use the right graph of the production rules to replace the handle to generate the reconstructed production operation graph.

[0057] Further, in one embodiment, step 1 constructs a set of production formulas to describe the structural mode, semantic specification and debugging scope of the production operation graph model, specifically including:

[0058] Step 1-1, construct terminal production rules and non-terminal production rules;

[0059] The right ends of the terminal productions are all terminal nodes, which are used to provide prerequisites for graph conversion operations;

[0060] There is a non-terminal node on the right side of the non-terminal production, which is used to determine whether the production operation graph can be reduced to the initial graph, so as to detect whether there is an unexpected error in the graph;

[0061] Step 1-2, construct a reduction-only production; the reduction-only production defines the scope of the debuggable structure and is used to automatically eliminate the repairable errors. The right end of the reduction-only production is the subgraph with the repairable errors, and the left end is the new subgraph reconstructed after eliminating the errors.

[0062] Here, the repairable errors include errors caused by node interruption and irregular drawing of graph models, etc.

[0063] Further, in one embodiment, in step 2, the production operation graph model is used as input, and the production formula constructed in step 1 is used to perform a reduction operation on the main graph, and different types of errors are detected and processed respectively, specifically including:

[0064] Step 2-1, search the main graph for the right graph handle of the reduction production and the terminal production. If the handle is found, replace the handle with the left graph of the production and execute step 2-2, otherwise execute step 2-3;

[0065] Step 2-2, determine whether the production rule used is a reduction-only production rule. If so, it means that there is a repairable error in the main graph, and execute step 2-1; otherwise, push the production rule used and the embedded graph pointer into the handle stack, and execute step 2-1;

[0066] Step 2-3, if the current main image does not contain any repairable errors or all the repairable errors have been corrected, save the current main image;

[0067] Step 2-4, determine whether the current main image is the initial image, if so, execute step 3, otherwise execute step 2-5;

[0068] Step 2-5, search for the handles of the non-terminal production and the right graph of the terminal production in the main graph. If the handle is found, replace the handle with the left graph of the production and execute step 2-4. Otherwise, it indicates that the input graph is illegal, prompting an unexpected error, and the process stops.

[0069] Further, in one embodiment, step 3 specifically includes:

[0070] Step 3-1, determine whether the reduction-only production rule has been used in step 2. If so, execute step 3-2. Otherwise, it prompts that there is no error in the input graph and the process ends.

[0071] Step 3-2, starting from the main graph saved in step 2-3, determine whether there is a non-terminal node in the current main graph. If so, pop a production rule and the embedded graph pointer in the reduction from the handle stack, use the right end of the production rule to replace the handle indicated by the embedded graph pointer in the reduction, and execute step 3-2; otherwise, return to the current reconstructed main graph and the process ends.

[0072] Furthermore, in one embodiment, the method further comprises executing before step 2:

[0073] The production operation graph model is preprocessed, and the nodes in the production operation graph model are abstracted into entities, relationships, attributes and error nodes respectively.

[0074] In one embodiment, a production operation graph model reconstruction system based on graph grammar is provided, the system comprising:

[0075] The first module is used to build a set of production-based descriptions of the structural model, semantic specifications, and debugging scope of the production operation graph model;

[0076] The second module is used to take the production operation graph model as input, perform reduction operations on the main graph using the production rules built in the first module, detect different types of errors and handle them separately;

[0077] The third module is used to deduce the main graph processed by the second module according to the reverse process of the reduction operation recorded in the handle stack, pop out the production rules in the handle stack and the embedded graph pointers in the reduction, namely the handle pointers, one by one, and use the production rule right graph to replace the handle to generate the reconstructed production operation graph.

[0078] For the specific limitations of the production operation graph model reconstruction system based on the graph grammar method, please refer to the limitations of the production operation graph model reconstruction method based on the graph grammar method mentioned above, which will not be repeated here. Each module in the above-mentioned production operation graph model reconstruction system based on the graph grammar method can be implemented in whole or in part through software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0079] In one embodiment, a computer device is provided, including 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 following is achieved:

[0080] Step 1: Build a set of production rules to describe the structural model, semantic specifications, and debugging scope of the production operation graph model;

[0081] Step 2: Take the production operation graph model as input, use the production rules constructed in step 1 to perform reduction operations on the main graph, detect different types of errors and handle them separately;

[0082] Step 3, deduce the main graph processed in step 2 according to the reverse process of the reduction operation recorded in the handle stack, pop out the production rules in the handle stack and the embedded graph pointers in the reduction, i.e., the handle pointers, one by one, and use the right graph of the production rules to replace the handle to generate the reconstructed production operation graph.

[0083] For the specific limitations of each step, please refer to the limitations of the production operation graph model reconstruction method based on graph grammar above, which will not be repeated here.

[0084] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the computer program implements:

[0085] Step 1: Build a set of production rules to describe the structural model, semantic specifications, and debugging scope of the production operation graph model;

[0086] Step 2: Take the production operation graph model as input, use the production rules constructed in step 1 to perform reduction operations on the main graph, detect different types of errors and handle them separately;

[0087] Step 3, deduce the main graph processed in step 2 according to the reverse process of the reduction operation recorded in the handle stack, pop out the production rules in the handle stack and the embedded graph pointers in the reduction, i.e., the handle pointers, one by one, and use the right graph of the production rules to replace the handle to generate the reconstructed production operation graph.

[0088] For the specific limitations of each step, please refer to the limitations of the production operation graph model reconstruction method based on graph grammar above, which will not be repeated here.

[0089] As a specific example, in one of the embodiments, the present invention is further described in detail with reference to the accompanying drawings.

[0090] Figure 2 It is an entity relationship diagram model of a production operation diagram. The nodes drawn with dotted lines in the diagram indicate that there are repairable errors in the node, including errors caused by node interruption and irregular drawing of the diagram model. Figure 3 It is a set of production rules for debugging graph grammar. The variable i is used to represent the type of each production rule: i=1 is a non-terminal production rule; i=0 is a terminal production rule; i=-1 is a reduction-only production rule. The three production rules together describe the structural pattern, semantic specification and debugging scope of the production operation graph model. Among them, Figure 3The symbols in the figure are as follows: P1 to P16 represent the numbers of productions respectively, E, A, R, and F are all node symbols, E, A, and R represent the abstraction of entity, attribute, and relationship subgraphs respectively, and F is a repairable error symbol obtained in the preprocessing stage based on prior knowledge; λ represents the initial graph of the grammar, which constitutes the starting state of the derivation process and the ending state of the reduction process (if the main graph is legal); ":=" is a component of the graph grammar production, and this symbol divides the production into left and right ends; "1*, 2*, 3*..." in the production hanging edge is a hanging edge marker, where the numbers "1, 2, 3" are used to describe the one-to-one correspondence between the hanging edges on the left and right ends of the production, ensuring the standardization of the graph replacement operation during the derivation and reduction process, and "*" after the number indicates that the hanging edge is a wildcard hanging edge, that is, it can match any number of edges in the main graph. Figure 4 It is the preprocessing process of the production operation graph model, which abstracts the nodes in the original graph model into entities, relationships, attributes, and error nodes to facilitate subsequent operations.

[0091] Figure 5 and Figure 6 They are two stages in the reduction process of the production operation graph. The first reduction stage uses terminal productions and reduction-only productions to eliminate the repairable errors in the graph; the second reduction stage uses terminal productions and non-terminal productions to detect whether there are unexpected errors in the graph that require manual intervention.

[0092] Figure 7 This is the process of deriving the production operation diagram. Figure 5 Starting from the main image after the correction of the correctable errors, follow Figure 6 The reverse process of the reduction process is used to derive the reconstructed production operation graph.

[0093] The whole process of the present invention has low reliance on human and data resources and is automatically executed by a computer, and provides a response measure for interruption recovery of the production operation system through graph reconstruction.

[0094] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments, and the above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for reconstructing a production operation graph model based on graph grammar, characterized in that: The method comprises: Step 1: Build a set of production rules to describe the structure, semantic specifications, and debugging scope of the production operation graph model; specifically, it includes: Step 1-1, construct terminal production rules and non-terminal production rules; The right ends of the terminal productions are all terminal nodes, which are used to provide prerequisites for graph conversion operations; There is a non-terminal node on the right side of the non-terminal production, which is used to determine whether the production operation graph can be reduced to the initial graph, so as to detect whether there is an unexpected error in the graph; Step 1-2, constructing a reduction-only production; the reduction-only production defines the scope of the debuggable structure, and is used to automatically eliminate the repairable errors; The right end of the reduction-only production is a subgraph with a repairable error, and the left end is a new subgraph reconstructed after eliminating the error; Step 2: Take the production operation graph model as input, use the production rules constructed in step 1 to perform reduction operations on the main graph, detect different types of errors and handle them separately; specifically: Step 2-1, search the main graph for the right graph handle of the reduction production and the terminal production. If the handle is found, replace the handle with the left graph of the production and execute step 2-2, otherwise execute step 2-3; Step 2-2, determine whether the production rule used is a reduction-only production rule. If so, it means that there is a repairable error in the main graph, and execute step 2-1; otherwise, push the production rule used and the embedded graph pointer into the handle stack, and execute step 2-1; Step 2-3, if the current main image does not contain any repairable errors or all the repairable errors have been corrected, save the current main image; Step 2-4, determine whether the current main image is the initial image, if so, execute step 3, otherwise execute step 2-5; Step 2-5, search for the handles of the non-terminal production and the right graph of the terminal production in the main graph. If the handle is found, replace the handle with the left graph of the production and execute step 2-4. Otherwise, it indicates that the input graph is illegal, prompting an unexpected error, and the process stops; Step 3, deduce the main graph processed in step 2 according to the reverse process of the reduction operation recorded in the handle stack, pop out the production rules in the handle stack and the embedded graph pointers in the reduction, i.e., the handle pointers, one by one, and use the right graph of the production rules to replace the handle to generate the reconstructed production operation graph.

2. The method for reconstructing a production operation graph model based on graph grammar according to claim 1, characterized in that: Step 3 specifically includes: Step 3-1, determine whether the reduction-only production rule has been used in step 2. If so, execute step 3-2. Otherwise, it prompts that there is no error in the input graph and the process ends. Step 3-2, starting from the main graph saved in step 2-3, determine whether there is a non-terminal node in the current main graph. If so, pop a production rule and the embedded graph pointer in the reduction from the handle stack, use the right end of the production rule to replace the handle indicated by the embedded graph pointer in the reduction, and execute step 3-2; otherwise, return to the current reconstructed main graph and the process ends.

3. The method for reconstructing a production operation graph model based on graph grammar according to claim 1 is characterized in that: The method further comprises executing, before step 2: The production operation graph model is preprocessed, and the nodes in the production operation graph model are abstracted into entities, relationships, attributes and error nodes respectively.

4. A production operation graph model reconstruction system based on graph grammar based on the method according to any one of claims 1 to 3, characterized in that: The system comprises: The first module is used to build a set of production-based descriptions of the structural model, semantic specifications, and debugging scope of the production operation graph model; The second module is used to take the production operation graph model as input, perform reduction operations on the main graph using the production rules built in the first module, detect different types of errors and handle them separately; The third module is used to deduce the main graph processed by the second module according to the reverse process of the reduction operation recorded in the handle stack, pop out the production rules in the handle stack and the embedded graph pointers in the reduction, namely the handle pointers, one by one, and use the production rule right graph to replace the handle to generate the reconstructed production operation graph.

5. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method according to any one of claims 1 to 3 is implemented.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 3 is implemented.

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