Order solution error positioning method, electronic device, storage medium and product

Abstract

The application discloses an order solving error positioning method, electronic equipment, storage medium and product, and relates to the technical field of digital manufacturing, and comprises the following steps: determining a digital process file of a current product based on a current product production order, acquiring an execution state of a process list and an execution state of a process rule in the digital process file in real time, and judging whether a preset abnormal condition exists based on the execution state of the process list and the execution state of the process rule; in the case that the preset abnormal condition exists, determining error data corresponding to the preset abnormal condition, determining an error position and an error cause based on the error data, and obtaining an error positioning result based on the error position and the error cause. Therefore, through an automatic and intelligent mode, the error positioning and analysis in the order solving process are realized, the problems that the manual positioning of the error position is time-consuming and inaccurate in the prior art are solved, and the technical effects of high production efficiency and low cost are realized.

Description

Technical Field

[0001] The present invention relates to the field of digital manufacturing technology, and in particular to an order calculation error positioning method, electronic equipment, storage medium and product. Background Art

[0002] In modern manufacturing, the growing adoption of digital process management systems (Teamcenter Manufacturing (TCM)) has significantly improved production efficiency and product quality. Through automated process document management and execution, TCM ensures standardized and consistent production processes. However, with the increasing complexity and diversity of production orders, TCM systems face numerous challenges in order resolution, particularly in error handling. The efficiency and accuracy of error handling are directly linked to the smoothness of the production process and product delivery time.

[0003] In related technologies, TCM systems rely primarily on manual analysis and location of error causes. However, due to the complexity and diversity of error messages, operators often need to identify potential problem points one by one. This process is not only time-consuming but also inaccurate in location, requiring urgent solutions. Summary of the Invention

[0004] The present invention provides an order solution error positioning method, electronic equipment, storage medium and product, so as to at least solve the problem of time-consuming and inaccurate positioning of manual error positioning in the prior art, and achieve the technical effect of high production efficiency and low cost.

[0005] The present invention provides an order settlement error location method, which is characterized by comprising the following steps:

[0006] Obtaining a current product production order, and determining a digital process file for the current product based on the current product production order, wherein the digital process file includes a process list and process rules;

[0007] Acquiring the execution status of the process list and the execution status of the process rules in real time, and judging whether a preset abnormal situation exists based on the execution status of the process list and the execution status of the process rules;

[0008] In the event that the preset abnormal situation exists, error data corresponding to the preset abnormal situation is determined, an error location and an error cause are determined based on the error data, and an error locating result is obtained based on the error location and the error cause.

[0009] The present invention provides an order solution error location device, which is characterized by comprising:

[0010] A first determination module is configured to obtain a current product production order and determine a digital process file of the current product based on the current product production order, wherein the digital process file includes a process list and process rules;

[0011] a judgment module, configured to obtain the execution status of the process list and the execution status of the process rules in real time, and judge whether there is a preset abnormal situation based on the execution status of the process list and the execution status of the process rules;

[0012] The second determination module is used to determine the error data corresponding to the preset abnormal situation when the preset abnormal situation exists, determine the error location and the error cause based on the error data, and obtain the error positioning result based on the error location and the error cause.

[0013] The present invention also provides an electronic device, comprising: a memory for storing a computer program; and a processor for implementing the steps of any of the above-mentioned order solution error locating methods when executing the computer program.

[0014] The present invention also provides a non-volatile computer-readable storage medium, in which a computer program is stored, wherein when the computer program is executed by a processor, the steps of any of the above-mentioned order resolution error locating methods are implemented.

[0015] The present invention also provides a computer program product, including a computer program, which, when executed by a processor, implements the steps of any of the above-mentioned order solution error locating methods.

[0016] Through the present invention, the digital process file of the current product is determined based on the current product production order, the execution status of the process list and the execution status of the process rules in the digital process file are obtained in real time, and based on the execution status of the process list and the execution status of the process rules, it is determined whether there is a preset abnormal situation; in the case of a preset abnormal situation, the error data corresponding to the preset abnormal situation is determined, and based on the error data, the error location and the error cause are determined, and the error location result is obtained based on the error location and the error cause. Thus, through an automated and intelligent approach, the rapid location and analysis of error problems in the order solution process are achieved, solving the problem of time-consuming and inaccurate manual location of the error location in the prior art, and achieving high production efficiency and low-cost technical effects. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the embodiments of the present invention, the following is a brief introduction to the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0018] Figure 1 A flowchart of a method for locating order error resolution provided by an embodiment of the present invention;

[0019] Figure 2 A schematic diagram of the data collection and monitoring process provided by an embodiment of the present invention;

[0020] Figure 3 A schematic diagram of the early warning prompt process provided by an embodiment of the present invention;

[0021] Figure 4 A schematic diagram of a checklist inspection process according to an embodiment of the present invention;

[0022] Figure 5 A schematic diagram of an optimized data verification mechanism provided by an embodiment of the present invention;

[0023] Figure 6 A block diagram of an order processing error location device provided by an embodiment of the present invention;

[0024] Figure 7 A schematic structural diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0025] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0026] It should be noted that, in the description of the present invention, the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, article, or apparatus comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus. The terms "first," "second," etc., in the present invention are used to distinguish similar objects, and are not used to describe a particular order or precedence.

[0027] In order to enable those skilled in the art to better understand the solutions of the present invention, the present invention is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0028] An embodiment of the present invention provides an order settlement error location method, and the method is described in detail in conjunction with the execution process of the order settlement error location method.

[0029] Figure 1 The present invention is a flowchart of an order settlement error location method according to an embodiment of the present invention.

[0030] Before introducing the order resolution error location method of an embodiment of the present invention, the order resolution error location system involved in the method is first introduced. The order resolution error location system consists of an input layer, a data processing layer, an application layer, and a data support layer. Among them, the input layer is used to receive the current product production order, trigger the system startup, and access the digital process file. The data processing layer includes a data acquisition and monitoring module and an error location module. The data acquisition and monitoring module is used to capture the execution status of the digital process file in real time, identify anomalies, and generate structured error data; the error location module includes an artificial intelligence learning unit and an information library matching unit. The artificial intelligence learning unit trains the early warning library based on historical error data and establishes an "error type-rule association" model. The information library matching unit can match the historical order error information library through keywords, rule feature comparison, etc. to quickly locate similar problems. The application layer includes an early warning and notification module and an error correction and optimization module. The early warning and notification module can analyze input rules in real time during the rule writing phase, proactively alerting users to potential risks. During the runtime phase, based on the results of the error location module, it can directly inform operators of potentially problematic rules. The error correction and optimization module can record problematic system orders and error information, establish an error correction library (accumulating rule optimization cases to form a reusable solution library), support intelligent retrieval and notification during rule writing, and / or automatically verify rule integrity to ensure that the rules meet the requirements for effectiveness. The data support layer includes a database module, which is used to store the early warning library, the historical order error information library, and the error correction library.

[0031] For example, Figure 1 As shown, the order solution error location method includes the following steps:

[0032] In step S101 , a current product production order is obtained, and a digital process file of the current product is determined based on the current product production order, wherein the digital process file includes a process list and process rules.

[0033] It can be understood that a current production order refers to the production instructions for a specific product currently being processed or about to be processed. It contains all the detailed information required to produce that product, such as product specifications, production quantity, production time, and required materials. The production order is the starting point of the production process and triggers the initiation of the entire production process. Digital process documentation refers to a set of detailed technical documents associated with the current production order. These documents, stored in digital form, guide the production process. Digital process documentation can include a bill of process (BOP) and a rule of process (ROP). The BOP defines the detailed steps for each process step in the production process, including the specific operations to be performed, the required materials, and the material feeding rules. The BOP is often used in conjunction with the BOM (Bill of Materials) to ensure that the materials required for each process step are correctly delivered according to the specified entry conditions or material feeding rules. Furthermore, the BOP involves animation production. This involves using animations to demonstrate the execution of process steps in a digital manufacturing or simulation environment for training, validation, or optimization of the production process. The BOP directly supports operator tasks during the production process. The ROP is a set of rules used to standardize and guide material installation. It contains all material installation rules and location identification information, primarily ensuring correct material installation during the production process. Based on the effective conditions or installation rules, the ROP identifies the material's installation location and captures the installation location within the 3D model of the entire machine for subsequent verification, recording, and archiving. The ROP ensures correct material installation and product quality.

[0034] Specifically, in an embodiment of the present invention, the current product production order is an object automatically identified and processed by the system. When a new production order (i.e., the current product production order) is issued, the system can automatically obtain the corresponding digital process files (i.e., BOP and ROP) based on the information in the production order, such as the model and specifications of the current product, to guide the production process.

[0035] In step S102 , the execution status of the process list and the execution status of the process rules are acquired in real time, and based on the execution status of the process list and the execution status of the process rules, it is determined whether a preset abnormal situation exists.

[0036] Specifically, in the digital manufacturing process, the system's real-time monitoring and anomaly detection functions are key to ensuring smooth production. Figure 2As shown in the figure, after obtaining the current product's BOP and ROP, the execution status of the BOP and ROP can be obtained and analyzed in real time. This status information provides detailed information about each step and rule in the product production process, helping the system to promptly identify and address potential issues. The BOP execution status can include material feeding status (i.e., monitoring whether each material is correctly fed into the corresponding process according to the effective conditions or feeding rules. For example, the system will check whether the material arrives at the designated location on time and whether the feeding quantity meets the requirements); process execution status (i.e., monitoring whether each process is progressing according to plan, including the process's start time, end time, and duration. The system will check whether the process is completed on time and whether there are any delays or interruptions); and assembly animation status (i.e., monitoring whether the assembly animation associated with each process is playing correctly to ensure that the operator can correctly perform the operation according to the animation instructions). The ROP execution status can include installation location identification status (i.e., monitoring whether the material's installation location can be correctly identified according to the validation conditions or installation rules. For example, the system will check whether the material's installation location is consistent with the designed location in the 3D model); screenshot status (i.e., monitoring whether the system can successfully generate a screenshot of the material's installation location. For example, the system will check whether the screenshot function is working properly and whether the screenshot is clear and meets the requirements); and validation condition status (i.e., monitoring whether the validation conditions are met to ensure the correct execution of the rule. For example, the system will check whether the parameters in the validation conditions are within the allowable range and whether the conditions are correctly triggered). Based on the acquired execution status information, it is possible to determine in real time whether there are preset anomalies, such as the inability to capture a screenshot (i.e., the inability to generate a screenshot of the material's installation location); material feeding failure (i.e., the inability to correctly feed the material into the corresponding process according to the feeding rules); the inability to find the corresponding process step (BOP) according to the validation conditions or feeding rules (e.g., the feeding rules do not match the actual production requirements); and the inability to correctly identify the material's process rule (ROP) according to the validation conditions or installation rules (e.g., the material's installation rules do not match the actual production requirements).

[0037] In step S103, when a preset abnormal situation exists, error data corresponding to the preset abnormal situation is determined, the error location and the error cause are determined based on the error data, and an error locating result is obtained based on the error location and the error cause.

[0038] Specifically, once a preset abnormal situation is detected, the system can record the error data corresponding to the preset abnormal situation in detail and provide timely feedback to the operator, clearly pointing out the rules that may have problems (i.e., the error location, such as "ROP installation rule line 15 has an effective condition error"), and the corresponding possible causes of this abnormal situation (i.e., the cause of the error), and based on the error location and error cause, summarize the final error location results.

[0039] Optionally, in some embodiments, the error reporting data includes at least one of error reporting time, material, error reporting type and related rules.

[0040] Specifically, error data is structured data consisting of the error time, the materials involved, the error type, and the associated rules. The error time refers to the specific point in time when the error occurred, which is crucial for analyzing error frequency, duration, and related temporal patterns. For example, if an error consistently occurs within a specific time period, it may indicate issues with specific production conditions or operational procedures during that time period. Information about the materials involved can include material type, batch, and supplier. This helps determine whether quality issues with a specific material are causing the error or whether adjustments to the handling of that material are necessary. The error type categorizes the error as either a BOP error or a ROP error. Clarifying the error type helps quickly identify the nature of the problem and select the appropriate resolution strategy. For example, an ROP error may involve issues with material installation location identification, while a BOP error may involve issues with feeding rules or process execution. The associated rules refer to the specific rules or configurations involved when the error occurs, such as specific rules within the ROP or BOP. This helps directly pinpoint the source of the problem, specifically which rules or settings may have caused the error.

[0041] Therefore, detailed error reporting allows operators to directly address the core of the problem rather than blindly troubleshooting possible causes. This significantly reduces the time and resources required to solve the problem and improves the overall efficiency of the system.

[0042] Next, we will explain in detail how to determine the error location and cause based on the error data.

[0043] As a possible implementation method, in some embodiments, the error location and the cause of the error are determined based on the error data, including: determining the key features based on the error data; matching the key features with the information in the preset early warning library to obtain a first initial matching result, and judging whether the first initial matching result meets the preset matching success condition; if the first initial matching result meets the preset matching success condition, a first matching result is obtained; and determining the error location and the cause of the error based on the first matching result.

[0044] It can be understood that the preset warning library refers to a model of error patterns and rules obtained by learning and training error information in historical orders using artificial intelligence algorithms.

[0045] Specifically, if Figure 3As shown, during the operational error reporting phase, when the system detects an error, it extracts key features (such as "ROP error," "certain material type," "validation condition conflict") from the error data and intelligently matches these features with data in a pre-set early warning database. Based on patterns learned from historical data, it quickly associates and locates potentially problematic rules (for example, an incorrect ROP installation rule for a certain material under specific conditions). During this matching process, an initial matching result (i.e., a first initial matching result) is generated. This result indicates the similarity between the current error data and a specific error pattern in the pre-set early warning database. The validity of this first initial matching result is determined based on pre-set match success criteria. The pre-set match success criteria stipulate that a match is considered successful only if the similarity between the current error data and the error pattern in the pre-set early warning database exceeds a certain threshold. If the first initial matching result meets the pre-set match success criteria, the system uses the successfully matched error pattern as the first matching result. Based on this first matching result, the error location and cause can be determined (i.e., a passive prompt is generated and notified to the operator, such as "BOP material feeding rule line 5 condition is incomplete").

[0046] For example, if historical data repeatedly reports that a certain type of material cannot identify the installation location under specific effective conditions, and the error is related to an installation rule in the ROP, then when a similar error occurs again, the system can quickly locate the ROP rule.

[0047] By extracting key features and matching them with a pre-set warning library, the system can quickly locate the specific location and cause of the error, reducing operator troubleshooting time. Furthermore, by providing detailed error locations and causes, operators can directly address the issue, improving problem-solving efficiency.

[0048] Optionally, in other embodiments, after determining whether the first initial matching result satisfies the preset matching success conditions, it also includes: if the first initial matching result does not meet the preset matching success conditions, matching the key features with the information in the preset error information library to obtain a second initial matching result, and determining whether the second initial matching result meets the preset matching success conditions; if the second initial matching result meets the preset matching success conditions, obtaining a second matching result; and determining the error location and error cause based on the second matching result.

[0049] It can be understood that the preset error information database is a database that records historical order error information in detail, which includes specific information of each error situation, such as the error time, materials involved, error type, relevant rules, solutions, etc.

[0050] Specifically, if the first initial matching result does not meet the preset matching success conditions, it means that the current error data and the error patterns in the preset warning library are not similar enough, and the error location and cause cannot be directly determined. Then, the key features extracted from the error data can be matched with the data information in the preset error information library, and the preset error information library can be searched through keyword matching (such as "unable to take a screenshot"), rule feature matching (such as "complexity of effective conditions"), etc. to determine whether there are similar historical error records. During this matching process, a second initial matching result can be generated. This result can represent the similarity between the current error data and a historical error record in the preset error information library, and the validity of the second initial matching result can be determined based on the preset matching success conditions. If the second initial matching result meets the preset matching success conditions, the system can use the successfully matched historical error record as the second matching result. Based on the second matching result, the error location and cause can be determined.

[0051] Therefore, even if the match with the preset warning library fails, the system can still quickly locate the problem by matching with the preset error information library, thereby reducing the operator's troubleshooting time and further improving the comprehensiveness and accuracy of error processing.

[0052] It should be noted that the process of intelligently matching the key features in the error data with the data information in the preset early warning library, and the process of matching the key features in the error data with the data information in the preset error information library, can also be processed in parallel. When the first initial matching result and the second initial matching result both meet the preset matching success conditions, the artificial intelligence matching result (i.e., the first matching result) can be used preferentially to determine the error location and error cause.

[0053] As a result, the system can fully utilize the advantages of the two matching mechanisms to improve the efficiency and accuracy of error positioning.

[0054] Optionally, in other embodiments, after determining whether the second initial matching result meets the preset matching success condition, it also includes: if the second initial matching result does not meet the preset matching success condition, the error data is marked as a new type of error, and the new type of error is added to the preset error information library.

[0055] That is to say, if both the first initial matching result and the second initial matching result do not meet the preset matching success conditions, that is, no similar alarm pattern or historical error record is matched, then the current error data can be marked as a new type of error, and all relevant information of the error data can be recorded in detail, including the error time, materials involved, error type, related rules, etc., and the detailed information of the new type of error can be added to the preset error information library to update the database.

[0056] Because new error types are unprecedented, detailed analysis can be performed manually. Based on the recorded details, operators will manually troubleshoot and resolve the issue. Once resolved, they can record the solution in a pre-set error database, enabling the system to automatically identify and address similar errors in the future.

[0057] Therefore, by adding new types of errors to the preset error information library, the preset error information library can be continuously updated, thereby better adapting to changes in the production environment and improving the stability and reliability of the system.

[0058] The following details how to obtain the preset warning library.

[0059] As a possible implementation method, in some embodiments, before matching the key features with the information in the preset early warning library, it also includes: obtaining a historical order error data set, and preprocessing the historical order error data set to obtain a preprocessed historical order error data set; based on the preprocessed historical order error data set, using a preset artificial intelligence algorithm to establish a preset early warning library, wherein the preset early warning library stores the association between different error types and related rules.

[0060] Specifically, error information from historical orders (i.e., the historical order error dataset) is collected. This information includes detailed information such as the error time, the materials involved, the error type, the relevant rules, and the solution. This information collection process ensures that the collected data is complete and accurate, covering error records from different time periods and production orders to comprehensively reflect the system's error status. Subsequently, the historical order error dataset undergoes preprocessing, including data cleaning (removing invalid or erroneous data records), feature extraction, data labeling (for example, labeling the error type as "ROP error" or "BOP error" and indicating the specific relevant rules involved), and data normalization, to obtain the preprocessed historical order error dataset. Next, a model is trained using the preprocessed historical order error dataset using a pre-defined artificial intelligence algorithm, such as a machine learning algorithm (decision tree, random forest, support vector machine, etc.) or a deep learning algorithm (neural network). During the training process, the algorithm learns the correlation between error types and relevant rules, establishing error patterns. Model performance is evaluated through methods such as cross-validation to ensure accuracy and generalization, and model parameters are adjusted to optimize performance. The trained model and its learned error patterns are stored in the initial database, resulting in a pre-set warning library. Each entry in this library contains the key features of a specific error pattern (error time, involved materials, error type, and related rules), the corresponding error location and cause, and the corresponding solution or optimization suggestion for the error pattern.

[0061] It should be noted that the preset warning library is a dynamic database. As new error data is added, the system will regularly train and update the preset warning library to include the latest error patterns and solutions.

[0062] Therefore, by creating a preset warning library, the system can quickly identify and locate errors, reduce operator troubleshooting time, and improve production efficiency.

[0063] Furthermore, in some embodiments, the above-mentioned order resolution error locating method also includes: determining whether there is a rule writing requirement; if there is a rule writing requirement, generating the feeding rules and current process rules in the current process list based on the rule writing requirement; analyzing the feeding rules and current process rules in the current process list based on the preset early warning library and the preset error correction library, and generating an early warning prompt when it is identified based on the analysis results that the current writing content contains a preset error.

[0064] Specifically, the system can determine whether there is a need for rule writing in a variety of ways. For example, when a new production order is placed, the system will check whether there are BOPs and ROPs related to the order. If not, rule writing is required. Alternatively, when new materials, new process steps, or new production conditions appear in the production process, the system will determine whether existing rules need to be updated. If so, rule writing is required. Alternatively, operators can manually initiate rule writing requests through the system interface, for example, if they find that existing rules cannot meet production needs.

[0065] like Figure 3 As shown in the figure, when the system determines that rule writing is required (i.e., during the rule writing phase), it can write BOP feeding rules and ROP process rules based on actual needs, thereby generating the feeding rules and process rules for the current process list. During the writing process, the system analyzes the input rules (i.e., the feeding rules and process rules for the current process list) in real time. Combining common rule issues learned from historical error datasets in a preset warning library and previous rule error cases stored in a preset error correction library, it automatically identifies potential errors or inconsistencies in the currently written rules (the feeding rules and process rules for the current process list) and generates corresponding warnings. For example, if the preset warning library indicates that a certain type of validation condition setting is likely to prevent materials from finding their corresponding BOP, the system will automatically issue a warning when the operator sets similar validation conditions when writing a BOP rule, indicating a potential rule issue. Specifically, if the validation condition setting is unreasonable, the system will identify this error and issue a warning: "Validation condition conflict. Please check the validation condition setting in the rule."

[0066] Therefore, through the analysis of the preset early warning library and the preset error correction library, the system can identify potential errors in rule writing in advance and intercept them in advance, thereby reducing the occurrence of later errors and ensuring stable operation of the system.

[0067] The following describes in detail how to obtain the preset error correction library.

[0068] As a possible implementation method, in some embodiments, before analyzing the feeding rules and current process rules in the current process list based on the preset early warning library and the preset error correction library, it also includes: obtaining a historical order error data set, and based on the historical order error data set, recording at least one error content and at least one solution, wherein at least one error content corresponds one-to-one to at least one solution; feature-labeling the at least one error content and the at least one solution respectively to generate a preset error correction library.

[0069] Specifically, when building the pre-set error correction library, all error data from historical orders can be collected, including detailed information such as the error time, the materials involved, the error type, and the relevant rules. For each error, the corresponding solution or optimization suggestion is recorded. These solutions can be manually recorded by the operator or automatically generated by the system. Keywords (such as error type, material name involved, specific rule issue, etc.) are extracted from the error content and solution to create a keyword index to facilitate subsequent rapid retrieval and comparison. Each error content and its corresponding solution are annotated with features such as error type, materials involved, validation conditions, installation rules, and solution type (such as rule modification, condition adjustment, or function optimization). This organized error data and its solution are stored in the initial error correction library, creating the pre-set error correction library. Each record in the pre-set error correction library includes error information (error content) and the corresponding solution (optimization suggestion).

[0070] Furthermore, during the BOP and ROP programming process, the system can automatically search a preset error correction library based on the currently programmed rule content. If it finds a similar situation in the preset error correction library to a rule that has previously caused an error, it will promptly alert the operator to avoid repeating the error. For example, if the preset error correction library records a feeding rule that caused an error due to unreasonable condition settings, when the operator writes a feeding rule with similar complex conditions again, the system will alert the operator to the potential risks and provide suggestions for simplifying the conditions.

[0071] Therefore, by building a preset error correction library, real-time prompts and suggestions can be provided during the rule writing and error processing process to help operators quickly locate and solve problems, reduce repeated errors, and improve system stability and order resolution accuracy.

[0072] Optionally, in some embodiments, after generating the feeding rules and current process rules in the current process list, it also includes: establishing a first checklist for the current process list and a second checklist for the current process rules, wherein the first checklist and the second checklist both include preset effectiveness conditions corresponding to each rule; based on the first checklist and the second checklist, the feeding rules and the current process rules in the current process list are checked respectively to determine whether there are rules that do not meet the preset effectiveness conditions; in the case that there are rules that do not meet the preset effectiveness conditions, a modification prompt information is generated, so that the operator can modify the rules that do not meet the preset effectiveness conditions based on the modification prompt information.

[0073] It is understandable that after writing and generating the feeding rules and current process rules in the current process bill, in order to ensure the accuracy and effectiveness of the written process bill (BOP) and process rules (ROP), the system can establish a detailed checklist to strictly check the rules.

[0074] Specifically, a first checklist for the current process bill (BOP checklist) and a second checklist for the current process rules (ROP checklist) are established. The first checklist is a detailed checklist for the feeding rules in the current process bill, including the preset validity conditions for each feeding rule, such as material type, quantity, feeding time, and process steps. This ensures that each feeding rule meets all necessary validity conditions, thereby ensuring the accuracy and timeliness of feeding. The second checklist is a detailed checklist for the installation rules in the current process rules. This checklist includes the preset validity conditions for each installation rule, such as the material installation location, installation conditions (temperature, pressure, etc.), and validity time. This ensures that each installation rule meets all necessary validity conditions, thereby ensuring the correct installation of materials and product quality.

[0075] like Figure 4 As shown, after the feeding rules and current process rules in the current process list are written, the system can automatically check each rule according to the first checklist and the second checklist. That is, according to the first checklist, the feeding rules in the current process list are checked one by one to verify whether each rule meets the preset validity conditions. According to the second checklist, the installation rules in the current process rules are checked one by one to verify whether each rule meets the preset validity conditions. If a rule meets all the preset validity conditions, the system will mark the rule as valid; if a rule does not meet the preset validity conditions, the system will record the detailed information of the rule, including the specific conditions that are not met, and at the same time, generate detailed modification prompt information to inform the operator which specific rules do not meet the preset validity conditions and what modifications need to be made.

[0076] For example, for installation rules in the ROP, the second checklist can check whether they include material installation location identification rules, whether the activation conditions are clear, and whether the function of taking screenshots of the installation location in the entire machine 3D model is set up. For process steps in the BOP, the first checklist can check whether the material feeding rules are complete, whether the activation conditions are reasonable, and whether the correct assembly animation is associated. If a rule is found not to meet the conditions in the checklist, the system will prompt the operator to modify and improve it.

[0077] This demonstrates that by establishing a detailed checklist and rigorously reviewing the feed and process rules within the process list, the system ensures the accuracy and effectiveness of the rules. When a rule is found to not meet the pre-set validation conditions, the system generates detailed modification prompts to help operators quickly make changes, thereby improving rule quality, reducing errors, and optimizing the production process.

[0078] Optionally, in some embodiments, after the operator modifies the rules that do not meet the preset effectiveness conditions based on the modification prompt information, it also includes: obtaining the modified rules, and re-performing warning analysis and inspection on the modified rules based on the preset warning library, the preset error correction library, the first checklist and the second checklist.

[0079] That is to say, after the operator modifies the rules according to the modification prompt information, the system can re-analyze and check the modified rules through the preset warning library, preset error correction library, first checklist and second checklist to ensure the correctness of the rules and the stability of the system. At the same time, the data after the rule optimization can be verified through the subsequent actual order solution process, such as Figure 5 As shown, if the verification is valid (e.g., no further errors are reported), it is marked as an optimized version and fed back to the preset warning library, preset error correction library, and preset error information library for update. If the verification fails, the old data is retained and the problem is marked, triggering a re-optimization process to avoid the introduction of incorrect rules. This forms a closed loop from error detection, problem location, warning issuance, error correction, and re-monitoring, establishing self-evolutionary capabilities. For example, when a new material type's installation position identification error is reported, the system automatically updates the ROP checklist (process rule checklist) after recording the solution. This ensures that the 3D model coordinate mapping rules for this material are verified when subsequent similar rules are written, achieving long-term optimization of "discovering one error, solving a class of problems." The implementation of this series of steps is intended to continuously improve the system's stability and ensure the continuous improvement of order resolution accuracy.

[0080] Furthermore, in some embodiments, when obtaining the error location result based on the error location and the error cause, it also includes: judging whether there are multiple error locations and multiple error causes; if there are multiple error locations and multiple error causes, determining the target error location and target error cause based on preset priority rules.

[0081] It is understood that during the error location process, if there are multiple possible error locations and causes, the system can determine the final error location and cause (i.e., the target error location and cause) based on preset priority rules. This reduces unnecessary modifications. If the system directly prompts all possible error locations and causes, the operator will need to check and modify them one by one, significantly increasing workload and time costs. By determining the final error location and cause, the system can directly guide the operator to the most likely problem point, reducing unnecessary inspections and modifications. It also prevents the introduction of new errors. If the operator makes modifications one by one based on multiple possible error locations and causes, new errors or incorrect modifications may be introduced. By determining the final error location and cause, the system can provide more accurate guidance and reduce the risk of incorrect operation.

[0082] Next, we will explain in detail how to determine the target error location and target error cause based on the preset priority rules.

[0083] As a possible implementation method, in some embodiments, the target error location and target error cause are determined based on preset priority rules, including: assigning different weights to the error time, material, error type and related rules in the error data based on the preset priority rules; performing a comprehensive weight evaluation on multiple error locations and multiple error causes according to the weight of the error time, the weight of the material, the weight of the error type and the weight of the related rules, and taking the error location and error cause with the highest comprehensive weight value as the target error location and target error cause.

[0084] Determining the target error location and cause is a comprehensive assessment process that involves calculating and comparing the weights of multiple factors. Specifically, weights are assigned to errors at different time points based on the time of error occurrence. For example, errors that occurred recently (e.g., within the past 24 hours) receive a higher weight, such as 10; errors that occurred earlier (e.g., within the past week) receive a lower weight, such as 3. Weights are assigned to different materials based on their importance and criticality in the production process. For example, critical materials receive a higher weight, such as 10; minor materials receive a lower weight, such as 3. Weights are assigned to different error types based on their severity and impact. For example, BOP errors receive a higher weight, such as 10; ROP errors receive a medium weight, such as 5. Weights are assigned to different rules based on the complexity and importance of the associated rules. For example, critical rules (e.g., core process steps) receive a higher weight, such as 10; minor rules (e.g., auxiliary process steps) receive a lower weight, such as 3. For each possible error location and cause that is matched, a comprehensive calculation is performed based on the weight of factors such as the error type, the material involved, the error time, and the relevant rules. The comprehensive weight can be calculated using the following formula:

[0085] Comprehensive weight = w1·error type weight + w2·involved physical weight + w3·error time weight + w4·related rule weight;

[0086] Among them, w1, w2, w3, and w4 are weight coefficients of various factors and can be adjusted according to actual needs.

[0087] By comparing the comprehensive weights of all possible matched error locations and causes, the error location and cause with the highest comprehensive weight are selected as the target error location and target error cause.

[0088] In summary, the order processing error location method proposed in the embodiment of the present invention has at least the following beneficial effects:

[0089] (1) Based on the learning and analysis of historical error data by artificial algorithms, a preset warning library is built to achieve pattern matching. Combined with feature comparison of the historical error information library, the specific rules corresponding to the error (such as ROP installation rules or BOP feeding rules) can be quickly located, eliminating manual line-by-line investigation and improving location efficiency by more than 70%. For example, when a certain type of material fails to capture due to a conflict in the effective conditions, the system can link it to historical similar errors within milliseconds and directly locate the code line in the ROP where the effective conditions are set incorrectly.

[0090] (2) During the rule writing phase (BOP feeding rules / ROP process rules), the system scans the input content in real time and proactively prompts potential risk points based on common error patterns in the early warning database (such as conflicting conditions for effectiveness and missing material positioning rules) and historical failure cases in the error correction database. For example, when an operator sets an overly complex feeding rule, the system will automatically warn that "condition nesting exceeding three levels may cause BOP matching timeout." This prevents errors before the rule takes effect, reducing the probability of errors in subsequent order processing by 60%.

[0091] (3) Through the closed loop of "error collection - location analysis - error correction recording - rule optimization - re-verification", the system continuously accumulates new error data to update the preset warning library and preset error correction library, forming a self-evolution capability. For example, when a new material type's installation position recognition error is reported, the system will automatically update the check items in the ROP checklist (process rule checklist) after recording the solution, ensuring that the 3D model coordinate mapping rules of the material are mandatory when similar rules are written in the future, achieving long-term optimization of "finding one error, solving a class of problems".

[0092] (4) The BOP checklist (Bill of Processes) and ROP checklist (Rule of Processes) clearly define the necessary conditions for each rule to take effect (e.g., the ROP must include trigger logic for material location screenshots, and the BOP must be associated with the corresponding assembly animation ID). The system automatically verifies each rule item to avoid runtime errors caused by missing rule elements. Compared to manual experience-based checks, standardized checks have increased the rule completeness pass rate from 85% to 99%, ensuring the standardization and enforceability of process documents.

[0093] (5) The preset warning library, preset error information library, and preset error correction library built based on historical data can transform expert experience into digital rules that can be executed by the system. Even if a new operator writes rules, the system can avoid common errors through real-time prompts. For example, when a novice sets a rule for taking screenshots of the material installation location, the system will automatically recommend the standardized positioning method of "preferentially using the entire machine coordinate system + component ID" based on the error correction library, reducing errors caused by non-standard rule design, thereby achieving the dual benefits of lowering the operating threshold and reducing the error rate.

[0094] (6) The method of the present invention can cover the entire life cycle of order resolution: from real-time monitoring (data collection) after the order is placed, intelligent positioning during operation (artificial intelligence + information database matching), active warning during rule writing (checklist + error correction prompts) to subsequent continuous optimization (closed-loop update), forming a full-link collaboration of "prevention-monitoring-solution-evolution". Compared with the traditional method of relying solely on post-investigation, it achieves systematic efficiency improvement and stability enhancement, and can be used in various assembly industries, including the automotive industry, parts industry, electronic products industry and other fields involving file changes.

[0095] According to the order resolution error location method proposed in the embodiment of the present invention, by determining the digital process file of the current product based on the current product production order, the execution status of the process list and the execution status of the process rules in the digital process file are obtained in real time, and based on the execution status of the process list and the execution status of the process rules, it is judged whether there is a preset abnormal situation; in the case of a preset abnormal situation, the error data corresponding to the preset abnormal situation is determined, and based on the error data, the error location and the error cause are determined, and the error location result is obtained based on the error location and the error cause. Thus, through an automated and intelligent approach, the rapid location and analysis of error problems in the order resolution process are achieved, solving the problem of time-consuming and inaccurate manual location of error locations in the prior art, and achieving high production efficiency and low-cost technical effects.

[0096] Through the description of the above implementation methods, those skilled in the art can clearly understand that the system according to the above embodiment can be implemented by means of software plus the necessary general hardware platform, and of course it can also be implemented by hardware, but in many cases the former is a better implementation method.

[0097] An embodiment of the present invention also provides an order resolution error locating device.

[0098] Figure 6 The figure is a block diagram of an order settlement error locating device according to an embodiment of the present invention.

[0099] like Figure 6 As shown, the order resolution error locating device 10 includes: a first determination module 100, a judgment module 200 and a second determination module 300.

[0100] The first determining module 100 is configured to obtain a current product production order and determine a digital process file of the current product based on the current product production order, wherein the digital process file includes a process list and process rules;

[0101] The judgment module 200 is used to obtain the execution status of the process list and the execution status of the process rules in real time, and judge whether there is a preset abnormal situation based on the execution status of the process list and the execution status of the process rules;

[0102] The second determination module 300 is used to determine the error data corresponding to the preset abnormal situation when there is a preset abnormal situation, determine the error location and error cause based on the error data, and obtain the error location result based on the error location and error cause.

[0103] Optionally, in some embodiments, the second determining module 300 includes:

[0104] A first determining unit, configured to determine a key feature based on the error reporting data;

[0105] A first judgment unit is used to match the key feature with the information in the preset early warning library to obtain a first initial matching result, and to judge whether the first initial matching result meets the preset matching success condition;

[0106] an obtaining unit, configured to obtain a first matching result if the first initial matching result satisfies a preset matching success condition;

[0107] The second determining unit is used to determine the error location and the error cause according to the first matching result.

[0108] Optionally, in some embodiments, after determining whether the first initial matching result satisfies a preset matching success condition, the first determining unit is further configured to:

[0109] If the first initial matching result does not meet the preset matching success condition, the key feature is matched with the information in the preset error information library to obtain a second initial matching result, and whether the second initial matching result meets the preset matching success condition is determined;

[0110] If the second initial matching result meets the preset matching success condition, a second matching result is obtained;

[0111] According to the second matching result, the error location and error cause are determined.

[0112] Optionally, in some embodiments, after determining whether the second initial matching result satisfies a preset matching success condition, the first determining unit is further configured to:

[0113] In the case that the second initial matching result does not meet the preset matching success condition, the error report data is marked as a new type of error report, and the new type of error report is added to the preset error report information library.

[0114] Optionally, in some embodiments, before matching the key feature with information in a preset warning library, the first judgment unit is further configured to:

[0115] Obtain a historical order error data set, and preprocess the historical order error data set to obtain a preprocessed historical order error data set;

[0116] Based on the preprocessed historical order error data set, a preset early warning library is established using a preset artificial intelligence algorithm, where the preset early warning library stores the association between different error types and related rules.

[0117] Optionally, in some embodiments, the error reporting data includes at least one of error reporting time, material, error reporting type and related rules.

[0118] Optionally, in some embodiments, the order settlement error locating device 10 further includes:

[0119] The second judgment unit is used to judge whether there is a rule writing requirement;

[0120] A generating unit, for generating feeding rules and current process rules in a current process list based on rule writing requirements when there is a rule writing requirement;

[0121] The analysis unit is used to analyze the feeding rules and current process rules in the current process list based on the preset early warning library and the preset error correction library, and generate an early warning prompt when it is identified based on the analysis results that there are preset errors in the current writing content.

[0122] Optionally, in some embodiments, before analyzing the feeding rules and the current process rules in the current process list based on the preset early warning library and the preset error correction library, the analyzing unit is further configured to:

[0123] Obtain a historical order error dataset, and based on the historical order error dataset, record at least one error content and at least one solution, wherein the at least one error content corresponds to the at least one solution in a one-to-one manner;

[0124] Feature annotation is performed on at least one error content and at least one solution respectively to generate a preset error correction library.

[0125] Optionally, in some embodiments, after generating the feeding rules and the current process rules in the current process list, the generating unit is further configured to:

[0126] Establishing a first checklist of the current process list and a second checklist of the current process rules, wherein both the first checklist and the second checklist include preset validity conditions corresponding to each rule;

[0127] Based on the first checklist and the second checklist, the material feeding rules and the current process rules in the current process list are checked respectively to determine whether there are any rules that do not meet the preset effectiveness conditions;

[0128] In the case that there are rules that do not meet the preset validity conditions, modification prompt information is generated, so that the operator can modify the rules that do not meet the preset validity conditions based on the modification prompt information.

[0129] Optionally, in some embodiments, after the operator modifies the rule that does not meet the preset validity condition based on the modification prompt information, the generating unit is further configured to:

[0130] The modified rules are obtained, and based on the preset early warning library, the preset error correction library, the first checklist, and the second checklist, the modified rules are re-analyzed and checked.

[0131] Optionally, in some embodiments, when the error location result is obtained based on the error location and the error cause, the second determining module 300 further includes:

[0132] The third judgment unit is used to judge whether there are multiple error reporting locations and multiple error reporting reasons;

[0133] The third determining unit is configured to determine a target error reporting location and a target error reporting cause based on a preset priority rule when there are multiple error reporting locations and multiple error reporting causes.

[0134] Optionally, in some embodiments, the third determining unit is specifically configured to:

[0135] Based on the preset priority rules, different weights are assigned to the error time, material, error type and related rules in the error data;

[0136] Based on the weight of the error reporting time, the weight of the material, the weight of the error reporting type and the weight of the relevant rules, a comprehensive weight evaluation is performed on multiple error reporting locations and multiple error reporting causes, and the error reporting location and error reporting cause with the highest comprehensive weight value are used as the target error reporting location and target error reporting cause.

[0137] According to the order resolution error locating device proposed in the embodiment of the present invention, by determining the digital process file of the current product based on the current product production order, the execution status of the process list and the execution status of the process rules in the digital process file are obtained in real time, and based on the execution status of the process list and the execution status of the process rules, it is determined whether there is a preset abnormal situation; in the case of a preset abnormal situation, the error data corresponding to the preset abnormal situation is determined, and based on the error data, the error location and the error cause are determined, and the error location result is obtained based on the error location and the error cause. Thus, through an automated and intelligent approach, the rapid location and analysis of error problems in the order resolution process are achieved, solving the problem of time-consuming and inaccurate manual location of the error location in the prior art, and achieving high production efficiency and low-cost technical effects.

[0138] Figure 7 This is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. The electronic device may include:

[0139] Memory 701 , processor 702 , and computer programs stored in the memory 701 and executable on the processor 702 .

[0140] When the processor 702 executes the program, the steps in any of the above-mentioned order settlement error location method embodiments are implemented.

[0141] Furthermore, the electronic device further includes:

[0142] The communication interface 703 is used for communication between the memory 701 and the processor 702 .

[0143] The memory 701 is used to store computer programs that can be run on the processor 702 .

[0144] The memory 701 may include a high-speed RAM (Random Access Memory) memory, and may also include a non-volatile memory, such as at least one disk memory.

[0145] If the memory 701, processor 702, and communication interface 703 are implemented independently, the communication interface 703, memory 701, and processor 702 can be connected to each other via a bus and communicate with each other. The bus can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, or an EISA (Extended Industry Standard Architecture) bus. Buses can be divided into address buses, data buses, control buses, etc. For ease of representation, Figure 7 Only one thick line is used in the diagram, but this does not mean that there is only one bus or one type of bus.

[0146] Optionally, in a specific implementation, if the memory 701, the processor 702 and the communication interface 703 are integrated on a chip, the memory 701, the processor 702 and the communication interface 703 can communicate with each other through an internal interface.

[0147] The processor 702 may be a CPU (Central Processing Unit), or an ASIC (Application Specific Integrated Circuit), or one or more integrated circuits configured to implement the embodiments of the present invention.

[0148] An embodiment of the present invention also provides a non-volatile computer-readable storage medium, which stores a computer program, wherein the computer program is configured to execute the steps of any of the above-mentioned order resolution error locating method embodiments when running.

[0149] In an exemplary embodiment, the non-volatile computer-readable storage medium may include, but is not limited to, various media that can store computer programs, such as a USB flash drive, a read-only memory (ROM), a random access memory, a mobile hard disk, a magnetic disk, or an optical disk.

[0150] An embodiment of the present invention further provides a computer program product, which includes a computer program. When the computer program is executed by a processor, it implements the steps of any of the above-mentioned order resolution error locating method embodiments.

[0151] An embodiment of the present invention also provides another computer program product, including a non-volatile computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the steps of any of the above-mentioned order resolution error locating method embodiments.

[0152] Professionals may further appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the above description has generally described the components and steps of each example according to their functions. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians may use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present invention.

[0153] The above is a detailed introduction to the order resolution error location method provided by the present invention. This article uses specific examples to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea. It should be pointed out that for ordinary technicians in this technical field, without departing from the principles of the present invention, the present invention can also be improved and modified in several ways, and these improvements and modifications also fall within the scope of protection of the claims of the present invention.

Claims

1. A method for locating order error calculation, characterized in that: The following steps are involved: Obtaining a current product production order, and determining a digital process file for the current product based on the current product production order, wherein the digital process file includes a process list and process rules; Acquiring the execution status of the process list and the execution status of the process rules in real time, and judging whether a preset abnormal situation exists based on the execution status of the process list and the execution status of the process rules; In the case where the preset abnormal situation exists, determining error data corresponding to the preset abnormal situation, determining the error location and the error cause based on the error data, and obtaining an error location result based on the error location and the error cause; The process list includes material feeding rules and process steps, and the process rules include material installation rules and location identification rules; The error reporting data includes at least one of error reporting time, material, error reporting type, and related rules, wherein the error reporting type includes process list error reporting and process rule error reporting, and the related rules refer to the rules or configurations involved in the preset abnormal situation, wherein the rules involved in the preset abnormal situation refer to the rules in the process list or the rules in the process rules; The determining of the error location and the error cause based on the error data includes: determining a key feature based on the error data, matching the key feature with information in a preset early warning library to obtain a first initial matching result, and determining whether the first initial matching result meets a preset matching success condition; if the first initial matching result meets the preset matching success condition, obtaining a first matching result; and determining the error location and the error cause based on the first matching result; After determining whether the first initial matching result satisfies the preset matching success condition, it also includes: if the first initial matching result does not meet the preset matching success condition, matching the key feature with the information in the preset error information library to obtain a second initial matching result, and determining whether the second initial matching result meets the preset matching success condition; if the second initial matching result meets the preset matching success condition, obtaining a second matching result; and determining the error location and the error cause based on the second matching result.

2. The order settlement error location method according to claim 1, characterized in that: After determining whether the second initial matching result satisfies the preset matching success condition, the method further includes: If the second initial matching result does not meet the preset matching success condition, the error report data is marked as a new type of error report, and the new type of error report is added to the preset error report information library.

3. The order settlement error location method according to claim 1, characterized in that: Before matching the key features with the information in the preset warning library, the method further includes: Acquire a historical order error data set, and preprocess the historical order error data set to obtain a preprocessed historical order error data set; Based on the pre-processed historical order error data set, the preset warning library is established using a preset artificial intelligence algorithm, wherein the preset warning library stores the association relationship between different error types and related rules.

4. The order settlement error location method according to claim 1, characterized in that: Also includes: Determine whether there is a need for rule writing; In the case where the rule writing requirement exists, generating the feeding rules and the current process rules in the current process list based on the rule writing requirement; Based on a preset early warning library and a preset error correction library, the feeding rules in the current process list and the current process rules are analyzed, and when it is identified based on the analysis results that the current written content has a preset error, an early warning prompt is generated.

5. The order settlement error location method according to claim 4 is characterized in that: Before analyzing the feeding rules and the current process rules in the current process list based on the preset early warning library and the preset error correction library, the method further includes: Obtaining a historical order error dataset, and recording at least one error content and at least one solution based on the historical order error dataset, wherein the at least one error content corresponds to the at least one solution in a one-to-one manner; Feature annotation is performed on at least one of the error content and at least one of the solutions to generate the preset error correction library.

6. The order settlement error location method according to claim 4 is characterized in that: After generating the feeding rules and the current process rules in the current process list, the method further includes: Establishing a first checklist for the current process list and a second checklist for the current process rules, wherein both the first checklist and the second checklist include preset validity conditions corresponding to each rule; Based on the first checklist and the second checklist, respectively checking the feeding rules and the current process rules in the current process list to determine whether there are any rules that do not meet the preset effectiveness conditions; In the case that there are rules that do not meet the preset validity conditions, modification prompt information is generated, so that the operator can modify the rules that do not meet the preset validity conditions based on the modification prompt information.

7. The order settlement error location method according to claim 6, characterized in that: After the operator modifies the rule that does not meet the preset validity condition based on the modification prompt information, the method further includes: The modified rules are obtained, and based on the preset early warning library, the preset error correction library, the first checklist, and the second checklist, the modified rules are re-analyzed and checked.

8. The order settlement error location method according to claim 1, characterized in that: When the error location result is obtained based on the error location and the error cause, the method further includes: Determine whether there are multiple error reporting locations and multiple error reporting reasons; In the case that there are multiple error reporting locations and multiple error reporting reasons, a target error reporting location and a target error reporting reason are determined based on a preset priority rule.

9. The order settlement error location method according to claim 8, characterized in that: The step of determining the target error location and target error cause based on the preset priority rule includes: Based on the preset priority rules, different weights are assigned to the error reporting time, material, error reporting type and related rules in the error reporting data; According to the weight of the error reporting time, the weight of the material, the weight of the error reporting type and the weight of the relevant rules, a comprehensive weight evaluation is performed on the multiple error reporting locations and the multiple error reporting reasons, and the error reporting location and error reporting reason with the highest comprehensive weight value are used as the target error reporting location and the target error reporting reason.

10. An electronic device, characterized in that: include: Memory for storing computer programs; A processor, configured to implement the steps of the order settlement error locating method as described in any one of claims 1 to 9 when executing the computer program.

11. A non-volatile computer-readable storage medium having a computer program stored thereon, characterized in that: The non-volatile computer-readable storage medium stores a computer program, wherein when the computer program is executed by a processor, the steps of the order settlement error locating method according to any one of claims 1 to 9 are implemented.

12. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the order settlement error locating method as described in any one of claims 1 to 9 are implemented.

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