Process data processing method, apparatus, and computer-readable storage medium

Abstract

This application provides a process data processing method, apparatus, and computer-readable storage medium. The method includes: responding to calculation logic input by a user in an interactive interface provided by a table-based calculation engine, performing lexical analysis and constructing an abstract syntax tree to generate a calculation instruction set; generating a version hash value for the calculation instruction set and storing it in association with an algorithm template; responding to a specification parameter change event of the target product, resolving the reference relationships between process nodes based on the abstract syntax tree to construct a directed acyclic graph; triggering cascaded recalculation of related nodes in the process algorithm template according to the topological sorting of the directed acyclic graph, and updating the process data; and converting the updated process data into structured bill of materials data through object relational mapping rules. This application significantly improves the controllability, consistency, traceability, and integration efficiency with the production system of process calculations, while taking into account both the agility of process design and the standardization of data management.

Description

Technical Field

[0001] This application relates to the field of computer-aided process design (CAPP) technology, specifically to a process data processing method, equipment, and computer-readable storage medium. Background Technology

[0002] In many customized manufacturing fields (such as wires and cables, automotive wiring harnesses, aerospace, electronics, custom furniture, and chemical formulations), product structures typically exhibit multi-level, multi-material, and multi-process characteristics. The calculation of process parameters and the generation of bills of materials heavily rely on engineering experience and mathematical formulas. These companies generally face a production model of "multiple varieties, small batches, and design-to-order," with frequent changes in process specifications and a complex interplay of national standards, company standards, and non-standard customer requirements. This makes process design a critical factor restricting response speed and data quality.

[0003] Currently, the industry primarily relies on offline spreadsheets for process calculations, with process engineers manually inputting formulas and parameters. While flexible, this approach suffers from inconsistent formula versions, difficulty in traceability, and unstructured data that cannot be integrated in real-time with systems such as Enterprise Resource Planning (ERP) and Manufacturing Execution System (MES). Some companies have attempted to introduce Computer-Aided Process Planning (CAPP) systems or hardcode formulas into databases, but these systems struggle to dynamically parse calculation logic or automatically identify dependencies and trigger cascading recalculations when formula logic is adjusted or parameters are changed, leading to data inconsistencies. Furthermore, version control of calculation logic is lacking, making it difficult to iterate and roll back process knowledge. Additionally, pushing calculation results to the production system requires manual transcription or periodic synchronization, lacking a real-time mapping mechanism.

[0004] Therefore, how to achieve dynamic parsing of calculation logic, version control, parameter linkage recalculation, and real-time mapping of structured data while retaining the flexibility of spreadsheets has become a technical problem that urgently needs to be solved in this field. Summary of the Invention

[0005] To address the aforementioned technical problems, this application provides a process data processing method, equipment, and computer-readable storage medium.

[0006] In a first aspect, embodiments of this application provide a process data processing method, the method comprising: responding to calculation logic input by a user in an interactive interface provided by a table calculation engine, performing lexical analysis on the calculation logic, constructing an abstract syntax tree, and generating a calculation instruction set based on the abstract syntax tree; generating a version hash value for the calculation instruction set, and storing the calculation instruction set and the version hash value in association to generate a process algorithm template; responding to a change event of the specification parameters of a target product, parsing the reference relationship between process nodes in the process algorithm template based on the abstract syntax tree, and constructing a directed acyclic graph; triggering cascaded recalculation of related nodes in the process algorithm template according to the topological sorting of the directed acyclic graph, and updating the process data; and converting the updated process data into structured bill of materials data through object relation mapping rules.

[0007] The aforementioned method performs lexical analysis and abstract syntax tree construction on the user-input computational logic, generating a cacheable instruction set. This enables the system to dynamically parse complex formulas without requiring pre-setting or hard coding. By generating version hash values ​​for the computational instruction set and storing them in association with the algorithm template, version control and traceability of the process logic are achieved, solving the problem of version chaos in traditional discrete Excel spreadsheets. Based on the abstract syntax tree, the method parses the parameter reference relationships between process nodes and constructs a directed acyclic graph. Combined with topological sorting to trigger cascading recalculation, it ensures that when any specification parameter changes, only the affected nodes are updated accurately and sequentially, avoiding the performance loss and data inconsistency caused by full recalculation, and achieving dynamic linkage of process parameters. Finally, through object relational mapping rules, the updated process data is converted into structured bill of materials data in real time, opening up the data path from process calculation to enterprise resource planning systems or manufacturing execution systems, realizing an automated closed loop from design to production. In summary, this method significantly improves the controllability, consistency, and integration efficiency of process calculations while retaining the flexibility of spreadsheets.

[0008] In conjunction with the first aspect, in one implementation, the construction of the directed acyclic graph includes: parsing the parameter reference relationships in the abstract syntax tree corresponding to each process node, using the parameter reference relationships as edges and the process nodes as vertices to generate the directed acyclic graph.

[0009] In conjunction with the first aspect, in one implementation, triggering the cascade recalculation of associated nodes in the process algorithm template includes: recalculating the process data of all downstream nodes sequentially, starting from the changed node, according to the topological sorting result of the directed acyclic graph.

[0010] In conjunction with the first aspect, in one implementation, the step of converting the updated process data into structured bill of materials data through object-relational mapping rules includes: mapping the fields of the tabular data objects in memory to the data table fields contained in the structured bill of materials data according to a predefined mapping configuration file.

[0011] In conjunction with the first aspect, in one implementation, the method further includes: pushing structured bill of materials data to an enterprise resource planning system or a manufacturing execution system.

[0012] In conjunction with the first aspect, in one implementation, the method further includes: in response to an audit instruction for the process algorithm template, comparing the node differences of the abstract syntax tree between the current version and historical versions, and generating a logic change visualization report.

[0013] In conjunction with the first aspect, in one implementation, the table calculation engine is an embedded spreadsheet engine or a visual drag-and-drop rule engine.

[0014] In conjunction with the first aspect, in one implementation, the version hash value is the cryptographic hash value of the serialized representation of the abstract syntax tree.

[0015] Secondly, embodiments of this application provide a process data processing device, which includes a processor, a memory, and a process data processing program stored in the memory and executable by the processor, wherein when the process data processing program is executed by the processor, it implements the process data processing method described in the first aspect.

[0016] Thirdly, embodiments of this application provide a computer-readable storage medium storing a process data processing program, wherein when the process data processing program is executed by a processor, it implements the process data processing method described in the first aspect.

[0017] The beneficial effects of the technical solutions provided in this application include: First, lexical analysis and abstract syntax tree construction are performed on the computational logic input by the user, enabling the system to dynamically parse complex formulas and generate cacheable instruction sets. This eliminates the reliance on pre-defined or hard-coded formulas in traditional solutions, improving the flexibility and maintainability of the process algorithm. Second, version hash values ​​are generated for the computational instruction set and stored in association with the algorithm template, achieving version control and full traceability of the process logic. This effectively solves the version confusion and knowledge loss problems caused by discrete Excel files. Third, based on the abstract syntax tree, the parameter reference relationships between nodes in each process are parsed and a directed acyclic graph is constructed. Combined with topological sorting to trigger cascading recalculation, this ensures that when specifications change, only the affected nodes are updated accurately and sequentially, avoiding the performance loss and data inconsistency caused by full recalculation, and achieving dynamic linkage of process parameters. Finally, through object relational mapping rules, the updated process data is converted into structured bill of materials data in real time, opening up an automated data path from process calculation to enterprise resource planning systems or manufacturing execution systems. In summary, this application significantly improves the controllability, consistency, traceability, and integration efficiency of process calculations with the production system while retaining the flexibility of spreadsheets, thus balancing the agility of process design with the standardization of data management. Attached Figure Description

[0018] Figure 1 This is a schematic flowchart of an embodiment of the process data processing method of this application; Figure 2 This is a schematic diagram of the hardware structure of the process data processing equipment involved in the embodiments of this application. Detailed Implementation

[0019] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present application.

[0020] To make the objectives, technical solutions, and advantages of this application clearer, the embodiments of this application will be described in further detail below with reference to the accompanying drawings.

[0021] Firstly, this application provides a process data processing method. It can be applied to process data processing in fields such as wires and cables, automotive wiring harnesses, aerospace vehicles, electronics, custom furniture, and chemical formulations. This embodiment uses the process data processing of wire and cable products as an example to explain in detail how to configure the process algorithm.

[0022] In one embodiment, reference is made to Figure 1 , Figure 1 This is a schematic flowchart of the first embodiment of the process data processing method of this application. Figure 1 As shown, the process data processing methods include: Step S10: In response to the calculation logic related to the process node input by the user in the interactive interface provided by the table calculation engine, perform lexical analysis on the calculation logic, construct an abstract syntax tree, and generate a set of calculation instructions based on the abstract syntax tree. In some embodiments, the process data processing system (a computing device or software program executing this method) can provide a lightweight, interactive interface based on Univer. The interactive interface provided by this spreadsheet calculation engine has a similar appearance to conventional spreadsheet software, allowing users (e.g., process engineers) to directly enter formulas into cells. For example, the interface includes cells corresponding to the process node "Insulation Extrusion Process". Process engineers input the calculation logic related to the process node "Insulation Extrusion Process" into the corresponding cell: Insulation Thickness = Conductor Diameter 0.05 + 0.2. In response to this input, the process data processing system invokes the lexical analyzer to perform lexical analysis on the string corresponding to the above calculation logic. The lexical analyzer breaks the string down into a series of tokens: insulation thickness (identifier), = (assignment operator), conductor diameter (identifier). (Multiplication operator), 0.05 (numeric literal), + (addition operator), 0.2 (numeric literal).

[0023] Subsequently, the parser constructs an Abstract Syntax Tree (AST) based on these tags according to operator precedence rules (e.g., multiplication has higher precedence than addition, and addition has higher precedence than assignment). In some embodiments, this AST is a binary tree.

[0024] In some embodiments, the root node of the abstract syntax tree is an assignment operator, and the root node has a left child node and a right child node. The left child node of the root node is the identifier "insulation thickness", and the left child node has no child nodes. The right child node of the root node is an addition operator (for ease of description, the right child node of the root node is simply referred to as the "addition node"), and the addition node has a left child node and a right child node. The right child node of the addition node is a numeric literal (0.2), and the right child node of the addition node has no child nodes. The left child node of the addition node is a multiplication operator (for ease of description, the left child node of the addition node is simply referred to as the "multiplication node"), and the multiplication node includes a left child node and a right child node. The left child node of the multiplication node is the identifier (conductor diameter), and the right child node of the multiplication node is a numeric literal (0.05).

[0025] The process data processing system generates an intermediate representation of the computation instruction set based on this abstract syntax tree and caches the instruction set in memory for efficient subsequent execution. For more complex computational logic (such as functions containing IF conditional statements or VLOOKUP lookup functions), the lexical analyzer generates an abstract syntax tree containing conditional branches or table lookup nodes, and generates branch instructions or table lookup instructions accordingly. In some embodiments, the process data processing system can traverse each node of the abstract syntax tree layer by layer using a post-order traversal method. Specifically, if a leaf node is reached and is an identifier, a parameter read instruction is generated accordingly; if the leaf node is a numeric literal, a constant load instruction is generated accordingly; if an operator node (e.g., the operator "=", "...") is reached, a parameter read instruction is generated accordingly. If the operator node is "+", then its left and right child nodes are processed first, generating calculation instructions corresponding to the left and right child nodes respectively. Then, the operation instructions corresponding to the current operator are generated. For multiplication nodes, multiplication operation instructions are generated; for addition nodes, addition operation instructions are generated; for assignment operator nodes, in addition to completing the numerical calculation of the expression on the right, variable storage instructions are also generated to store the calculated value into the storage location corresponding to the target variable on the left. Taking the formula in this embodiment as an example, the final calculation result is stored into the variable "insulation thickness". All the generated instructions are concatenated in the order of execution to obtain the final calculation instruction set.

[0026] Step S20: Generate a hash value for the computation instruction set (hereinafter referred to as "version hash value" for ease of description), and associate the computation instruction set with the version hash value for storage to generate a process algorithm template.

[0027] In some embodiments, the version hash is the cryptographic hash of the serialized representation of the abstract syntax tree.

[0028] In some embodiments, after completing the construction of the abstract syntax tree corresponding to each computational logic and the generation of the computational instruction set, the process data processing system will also serialize the abstract syntax tree and output serialized data, such as tree structure text in JSON format or string form; input the serialized data into a cryptographic hash function such as SHA-256 to generate a version hash value, an example version hash value of "3f4a5c2b…".

[0029] In some embodiments, the process data processing system also collects and records metadata, which includes, but is not limited to, information such as the creator of the process algorithm template, the creation time, and the model of the product to which it belongs.

[0030] In some embodiments, the process data processing system also generates a hash value (hereinafter referred to as the "global version hash value") based on the serialized data of the abstract syntax tree corresponding to all computational logic.

[0031] In some embodiments, the process data processing system also integrates the global version hash value, metadata, the computation instruction set corresponding to all computation logic, the version hash value corresponding to each computation logic, and the serialized data of the abstract syntax tree corresponding to each computation logic as a process algorithm template.

[0032] For example, taking a process link containing four process nodes—filament drawing, insulation extrusion, cabling, and sheath extrusion—as an example, each process node includes a corresponding computational logic. The process algorithm template includes a global version hash value, metadata, and relevant data corresponding to each process node. For example, the relevant data for the first process node (such as the filament drawing process) includes the first serialized data of the first abstract syntax tree of the first computational logic involved in the first process node, the first computational instruction set, and the first version hash value; the relevant data for the second process node (such as the insulation extrusion process) includes the second serialized data of the second abstract syntax tree of the second computational logic involved in the second process node, the second computational instruction set, and the second version hash value; the relevant data for the third process node (such as the cabling process) includes the third serialized data of the third abstract syntax tree of the third computational logic involved in the third process node, the third computational instruction set, and the third version hash value; and the relevant data for the fourth process node (such as the sheath extrusion process) includes the fourth serialized data of the fourth abstract syntax tree of the fourth computational logic involved in the fourth process node, the fourth computational instruction set, and the fourth version hash value.

[0033] In some embodiments, if the process engineers subsequently modify any of the computational logic (e.g., adjust the constant 0.2 to 0.25), the process data processing system can also respond to the modification signal by performing lexical analysis on the string corresponding to the modified computational logic to update the abstract syntax tree and the corresponding serialized data, updating the computational instruction set and version hash value, and updating the process algorithm template based on the updated computational logic, the serialized data of the updated abstract syntax tree, and the updated version hash value.

[0034] In some embodiments, the previous process algorithm template is still retained, that is, the previous abstract syntax tree and version hash value are still retained, thereby enabling version traceability and rollback.

[0035] Step S30: In response to the change event of the target product's specification parameters, construct a directed acyclic graph by parsing the reference relationships between process nodes in the process algorithm template based on the abstract syntax tree. In some embodiments, a process chain may include multiple process nodes, each of which may involve one or more computational logics. The process data processing system traverses the abstract syntax tree corresponding to all computational logics to determine the external parameters referenced in each computational logic (i.e., the output parameters of other process nodes). For example, the computational logic for "insulation thickness" related to the process node "insulation extrusion process" references "conductor diameter," which is determined by the computational logic "conductor diameter = SQRT (cross-sectional area)" involved in the process node "filament drawing process." It was calculated using 4 / π.

[0036] In some embodiments, the process data processing system constructs a directed acyclic graph (DAG) of the process algorithm template, using each process node as a vertex and constructing directed edges based on the parameter reference relationships between process nodes. In a DAG, a point from one vertex to another indicates that the latter vertex depends on the calculation result of the former vertex.

[0037] In some embodiments, a directed acyclic graph contains multiple vertices and multiple unidirectional directed edges. Vertices represent process nodes, and directed edges represent parameter reference relationships between processes. The directed acyclic graph does not contain any closed loop paths that are connected end to end, and the process compliance operation sequence of process nodes can be obtained through topological sorting.

[0038] In some embodiments, the process data processing system also uses a directed acyclic graph to detect whether there are cyclic dependencies (such as process node A depending on process node B, and process node B depending on process node A). If such dependencies exist, the system prompts the process engineer to correct the formula.

[0039] In some embodiments, the method for constructing a directed acyclic graph (DAG) using process data processing includes: setting vertices of the DAG based on each process node; creating unidirectional directed edges according to the parameter reference relationships between each process node; determining whether a closed-loop path exists based on the arrow directions of all directed edges; and if it is confirmed that no closed-loop path exists, performing topological sorting to obtain a compliant sequence of process operations. In some embodiments, if a closed-loop path is detected, a correction prompt indicating the existence of a circular dependency is issued.

[0040] For example, taking a process link containing four process nodes (such as wire drawing, insulation extrusion, cabling, and sheath extrusion), the four process nodes (i.e., process node "wire drawing", process node "insulation extrusion", process node "cabbing", and process node "sheath extrusion") can be set as vertices of a directed acyclic graph, that is, each vertex corresponds to a process node, which is used to carry all the calculation logic and output parameters under that process node; then, unidirectional directed edges are created according to the parameter reference relationship between process nodes. For example, the calculation logic of process node "insulation extrusion" references the parameter "conductor diameter" output by process node "wire drawing", so a directed edge is constructed from vertex "wire drawing" to vertex "insulation extrusion"; the calculation logic of process node "cabbing" references the parameter "insulation thickness" output by process node "insulation extrusion", so a directed edge is constructed from vertex "wire drawing" to vertex "insulation extrusion". The directed edge from the "insulation extrusion process" to the vertex "cable forming process" is constructed. The calculation logic of the process node "sheath extrusion process" references the parameter "cable outer diameter" output by the process node "cable forming process". Thus, a directed edge is constructed from the vertex "cable forming process" to the vertex "sheath extrusion process", forming a unidirectional link from the wire drawing process to the insulation extrusion process, then to the cable forming process, and then to the sheath extrusion process. Then, the path is traversed along the arrow direction of all directed edges. If it is only possible to go from the vertex corresponding to the previous process to the vertex corresponding to the next process, and there is no closed loop path that can return to the starting point, then it is determined that the requirement of no closed loop in the directed acyclic graph is met. Finally, the current directed acyclic graph is topologically sorted, and a compliant operation sequence is output. For example, the calculation logic of the wire drawing process is executed first. After the parameters are output, the calculation logic of the insulation extrusion process, cable forming process, and sheath extrusion process are executed in sequence. This ensures that the prerequisite parameters required for the calculation of downstream processes have been obtained.

[0041] In some embodiments, when a parameter involved in the calculation logic of the target product changes, for example, if the user changes the conductor cross-sectional area from 50mm² to 70mm², the process data processing system detects the change event and determines that the parameter affects the "wire drawing process". The process data processing system can then quickly identify all affected related processes, such as the "insulation extrusion process", "cabling process", and "sheath extrusion process", based on a pre-built directed acyclic graph.

[0042] Step S40: Based on the topological sorting of the directed acyclic graph, trigger the cascade recalculation of related nodes in the process algorithm template and update the process data.

[0043] In some embodiments, associated nodes are process nodes that have parameter reference relationships.

[0044] In some embodiments, the process data of all downstream nodes are recalculated sequentially from the changed node according to the topological sorting result of the directed acyclic graph. In some embodiments, the process node corresponding to the change in specification parameters is the changed node.

[0045] In some embodiments, to precisely control the recalculation order, the system performs topological sorting on the directed acyclic graph to obtain a linear sequence of calculations for process nodes. Taking the cable process described above as an example, the topological sorting result is: wire drawing process first, then insulation extrusion process, then cabling process, and finally sheath extrusion process. For example, when the conductor cross-sectional area changes, the system executes the calculation instruction set corresponding to each process node in this order: first, the wire drawing process is recalculated to obtain the new conductor diameter; then the insulation extrusion process is triggered to calculate the insulation thickness using the new conductor diameter; then the cable outer diameter, sheath thickness, etc., are updated sequentially. After each process node is recalculated, its result is immediately updated to the data object in memory and can be used for the calculation of downstream process nodes. This mechanism avoids full recalculation and only updates downstream process nodes affected by changes, greatly improving computational efficiency.

[0046] Step S50: Convert the updated process data into structured bill of materials data using object relation mapping rules.

[0047] In some embodiments, converting updated process data into structured bill of materials data through object-relational mapping rules includes: mapping fields of in-memory tabular data objects to data table fields contained in the structured bill of materials data, according to a predefined object-relational mapping configuration file.

[0048] In some embodiments, after all cascaded recalculations are completed, a complete tabular data object exists in memory, containing process parameters for each process node (such as conductor diameter, insulation thickness, material usage, etc.). The system predefines a set of Object Relational Mapping (ORM) configuration files, for example using XML or annotations, to map the fields in the tabular data object to the data table fields required by the Enterprise Resource Planning (ERP) system's Bill of Materials (BOM).

[0049] The system reads the mapping configuration through an ORM framework (such as Hibernate or MyBatis) and converts the in-memory tabular data object instances into a structured data model (such as Java POJO or JSON) that the ERP system can recognize. Subsequently, the system can push this structured bill of materials data to the ERP system's BOM interface in real time via a REST API or message queue, thereby completing an automated closed loop from process calculation to production data synchronization.

[0050] Furthermore, in one embodiment, the system supports an audit process before the release of the process algorithm template version. When process engineers submit a new version template (e.g., V1.1), the system compares the AST serialization data of the current version with that of the previous version (V1.0), traverses the process nodes, and marks the differences: added process nodes, deleted process nodes, and modified process nodes (including operator changes, constant changes, and parameter reference changes). The process data processing system generates a logic change visualization report based on the differences, showing in chart form which formulas have been modified and which downstream process nodes the modifications may affect. Auditors assess the risk of changes based on this report. The process data processing system also presets a set of safety rules, such as "deleting safety-related parameters is prohibited" and "the change in constants must not exceed 20%". Only when the changes comply with the safety rule set will the process data processing system allow the version to be marked as "released"; otherwise, it will be returned for modification. This mechanism ensures the controlled evolution of the algorithm logic.

[0051] The above embodiments are merely preferred implementations of the present invention, and equivalent transformations made by those skilled in the art based on the concept of the present invention all fall within the protection scope of the present invention. The spreadsheet calculation engine can be Univer, Luckysheet, or other embedded spreadsheet engines; ORM mapping can also be replaced by other data transformation technologies (such as manually constructing DTOs). Any variations that do not depart from the core technical features of the present invention (AST parsing, version hashing, DAG cascade recalculation, ORM structured output) should be considered as implementations of the present invention.

[0052] Secondly, embodiments of this application provide a process data processing device 20, which may be a personal computer (PC), laptop computer, server, or other device with data processing capabilities.

[0053] Reference Figure 2 , Figure 2 This is a schematic diagram of the hardware structure of the process data processing device involved in the embodiments of this application. In the embodiments of this application, the process data processing device may include a processor 21, a memory 22, a communication interface 23, and a communication bus 24.

[0054] The communication bus 24 can be of any type and is used to interconnect the processor 21, the memory 22 and the communication interface 23.

[0055] Communication interface 23 includes input / output (I / O) interfaces, physical interfaces, and logical interfaces for interconnecting devices within the process data processing equipment, as well as interfaces for interconnecting the process data processing equipment with other devices (such as other computing devices or user equipment). Physical interfaces can be Ethernet interfaces, fiber optic interfaces, ATM interfaces, etc.; user equipment can be displays, keyboards, etc.

[0056] The memory 22 can be various types of storage media, such as random access memory (RAM), read-only memory (ROM), non-volatile RAM (NVRAM), flash memory, optical storage, hard disk, programmable ROM (PROM), erasable PROM (EPROM), electrically erasable PROM (EEPROM), etc.

[0057] Processor 21 can be a general-purpose processor, which can call the process data processing program stored in memory 22 and execute the process data processing method provided in the embodiments of this application. For example, the general-purpose processor can be a central processing unit (CPU). The method executed when the process data processing program is called can be referred to in the various embodiments of the process data processing method of this application, and will not be described again here.

[0058] Those skilled in the art will understand that Figure 2 The hardware structure shown does not constitute a limitation of this application and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0059] Thirdly, embodiments of this application also provide a computer-readable storage medium.

[0060] The present application has a computer-readable storage medium storing a process data processing program, wherein when the process data processing program is executed by the processor 21, it implements the steps of the process data processing method as described above.

[0061] The method implemented when the process data processing program is executed can be referred to in various embodiments of the process data processing method of this application, and will not be repeated here.

[0062] It should be noted that the sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0063] The terms "comprising" and "having," and any variations thereof, in the specification, claims, and accompanying drawings of this application are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to such process, method, product, or apparatus. The terms "first," "second," and "third," etc., are used to distinguish different objects, etc., and do not indicate a sequence, nor do they limit "first," "second," and "third" to different types.

[0064] In the description of the embodiments of this application, terms such as "exemplary," "for example," or "for instance" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as "exemplary," "for example," or "for instance" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of terms such as "exemplary," "for example," or "for instance" is intended to present the relevant concepts in a concrete manner.

[0065] In the description of the embodiments of this application, unless otherwise stated, " / " means "or". For example, A / B can mean A or B. The "and / or" in the text is merely a description of the relationship between related objects, indicating that there can be three relationships. For example, A and / or B can mean: A exists alone, A and B exist simultaneously, and B exists alone. In addition, in the description of the embodiments of this application, "multiple" means two or more.

[0066] In some processes described in the embodiments of this application, multiple operations or steps are included in a specific order. However, it should be understood that these operations or steps may not be executed in the order they appear in the embodiments of this application, or they may be executed in parallel. The sequence number of the operation is only used to distinguish different operations, and the sequence number itself does not represent any execution order. In addition, these processes may include more or fewer operations, and these operations or steps may be executed sequentially or in parallel, and these operations or steps may be combined.

[0067] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above, and includes several instructions to cause a terminal device to execute the methods of the various embodiments of this application.

[0068] The above are merely preferred embodiments of this application and do not limit the patent scope of this application. Any equivalent structural or procedural transformations made using the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.

Claims

1. A method for processing process data, characterized in that, The method includes: In response to the calculation logic input by the user in the interactive interface provided by the table calculation engine, the calculation logic is lexically analyzed, an abstract syntax tree is constructed, and a set of calculation instructions is generated based on the abstract syntax tree; A version hash value is generated for the computation instruction set, and the computation instruction set is associated with and stored with the version hash value. The version hash value is an encrypted hash value of the serialized representation of the abstract syntax tree. Metadata is collected and recorded, including the creator, creation time, and product model of the process algorithm template. A hash value is generated as a global version hash value based on the serialized data of the abstract syntax trees corresponding to all computation logics. The global version hash value, the metadata, the computation instruction sets corresponding to all computation logics, the version hash value corresponding to each computation logic, and the serialized data of the abstract syntax trees corresponding to each computation logic are integrated as the process algorithm template. In response to a change event in the specifications of the target product, the reference relationships between process nodes in the process algorithm template are parsed based on the abstract syntax tree to construct a directed acyclic graph; Based on the topological sorting of the directed acyclic graph, the cascade recalculation of related nodes in the process algorithm template is triggered to update the process data; The updated process data is converted into structured bill of materials data using object-relational mapping rules.

2. The process data processing method as described in claim 1, characterized in that, The construction of the directed acyclic graph includes: The parameter reference relationships in the abstract syntax tree corresponding to each process node are parsed, and the directed acyclic graph is generated using the parameter reference relationships as edges and the process nodes as vertices.

3. The process data processing method as described in claim 1, characterized in that, The triggering of cascaded recalculation of associated nodes in the process algorithm template includes: Based on the topological sorting result of the directed acyclic graph, the process data of all downstream nodes are recalculated sequentially starting from the changed node.

4. The process data processing method as described in claim 1, characterized in that, The process of converting the updated process data into structured bill of materials data through object relation mapping rules includes: Based on a predefined mapping configuration file, the fields of the in-memory tabular data objects are mapped to the data table fields contained in the structured bill of materials data.

5. The process data processing method as described in claim 1, characterized in that, The method further includes: Push the structured bill of materials data to the enterprise resource planning system or manufacturing execution system.

6. The process data processing method as described in claim 1, characterized in that, The method further includes: In response to the review instruction for the process algorithm template, the differences in nodes of the abstract syntax tree between the current version and the historical version are compared to generate a logic change visualization report.

7. The process data processing method as described in claim 1, characterized in that, The table calculation engine is either an embedded spreadsheet engine or a visual drag-and-drop rule engine.

8. A process data processing device, characterized in that, The process data processing device includes a processor, a memory, and a process data processing program stored in the memory and executable by the processor, wherein when the process data processing program is executed by the processor, it implements the process data processing method as described in any one of claims 1 to 7.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a process data processing program, wherein when the process data processing program is executed by a processor, it implements the process data processing method as described in any one of claims 1 to 7.

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