A method and management system for technical status management of rail transit gearbox products

Abstract

This application relates to the field of rail transit gearbox management technology, and in particular to a method and management system for managing the technical status of rail transit gearbox products. The method includes: creating a baseline task for managing the technical status of rail transit gearbox products based on preset technical status management rules; identifying task identifiers contained in the baseline task, and identifying multiple initial data to be filled corresponding to the baseline task from a preset multi-source heterogeneous database based on the task identifiers; identifying the data source, part level, and data quality characteristics corresponding to each initial data to be filled, and performing data preprocessing on each initial data to be filled to obtain target data to be filled; identifying task decision identifiers from user task instructions, and determining a visualized technical status management table based on the task decision identifiers and the target data to be filled. This application facilitates improvements in the reliability and efficiency of managing data related to the technical status of rail transit gearbox products.

Description

Technical Field

[0001] This application relates to the field of rail transit gearbox management technology, and in particular to a method and management system for managing the technical status of rail transit gearbox products. Background Technology

[0002] As a transmission system of rail transit vehicles, the gearbox is one of the nine key components of the vehicle system and plays an important role in the smooth operation of the train. Therefore, the structure, assembly and testing process of rail transit gearbox products are complex and precise. Conventional management methods revolve around decentralized storage, manual linkage and standardized forms. Among them, relevant data are generally stored separately according to business links, relying on multiple independent systems for management, while data association mainly relies on manual matching, resulting in low traceability.

[0003] While conventional management methods can meet basic data storage and compliance requirements, given the complex structure, large data volume, and high traceability requirements of gearboxes, using conventional management methods to manage technical status data of rail transit gearbox products may result in defects such as data dispersion, weak correlation, lagging monitoring, and low efficiency. Consequently, conventional management methods may not be able to fully meet the high reliability and high efficiency management requirements of technical status data of rail transit gearbox products. Summary of the Invention

[0004] To improve the reliability and efficiency of managing technical status data of rail transit gearbox products, this application provides a method and system for managing the technical status of rail transit gearbox products.

[0005] Firstly, this application provides a method for managing the technical status of rail transit gearbox products, employing the following technical solution: A method for managing the technical status of rail transit gearbox products includes: Based on preset technical status management rules, create baseline tasks for the technical status management of rail transit gearbox products; Identify the task identifier contained in the baseline task, and identify multiple initial data to be filled corresponding to the baseline task from a preset multi-source heterogeneous database based on the task identifier. The preset multi-source heterogeneous database contains top-level native core data, mid-level collaborative data, and bottom-level potential data. Identify the data source, part level, and data quality characteristics corresponding to each initial data to be filled, and perform data preprocessing on each initial data to be filled based on the data source, part level, and data quality characteristics corresponding to each initial data to be filled to obtain the target data to be filled; When a user task instruction is detected, a task decision identifier is identified from the user task instruction, and a visualization technology status management table is determined based on the task decision identifier and the target data to be filled.

[0006] By adopting the above technical solution, baseline tasks are created based on preset technical status management rules, breaking down gearbox technical status management into a clear and implementable task framework. This ensures that all technical status data are collected around a unified task objective, avoiding management chaos caused by data dispersion, task overlap, and unclear responsibilities in conventional management. It guarantees the standardization of data management from the source. In addition, targeted data preprocessing based on the data type of the initial data to be filled helps avoid decision-making biases caused by manual entry errors, chaotic data formats, and invalid data interference in conventional management, ensuring the reliability of management results from the data quality perspective. Automatic data association is achieved through task identification without manual intervention, thereby improving the efficiency and accuracy of data collection. Finally, by integrating the target data to be filled into a visual status management table, relevant core content can be directly presented to users, enabling them to quickly obtain the required information and make corresponding decisions.

[0007] In one possible implementation, the initial data to be filled is preprocessed based on the data source, the part level, and the data quality characteristics corresponding to the initial data to be filled, including: A corresponding local knowledge graph is constructed based on the data source corresponding to each initial data to be filled, and the graph position and influencing node parameters of the initial data to be filled are identified from the local knowledge graph. The influencing node parameters include the number of influencing nodes and the type of influencing nodes. The importance level of data nodes is determined by quantizing and weighting the location of the data nodes and the parameters of the influencing nodes. Based on the mapping relationship between the part level and the preset part node importance level, the importance level of the part node corresponding to the part level is determined. The preset part node importance level mapping relationship is the correspondence between the part level and the importance level of the part node. The initial data to be filled is determined based on the importance level of the data node and the importance level of the part node, and the data processing strategy for the initial data to be filled is determined based on the mapping relationship between the filling level and the preset processing strategy. Based on the data quality characteristics and preset processing direction mapping relationship corresponding to the initial data to be filled, the data processing direction of the initial data to be filled is determined, and the preset processing direction mapping relationship is the correspondence between data quality characteristics and data processing direction. Based on the data processing strategy and data processing direction corresponding to the initial data to be filled, the initial data to be filled is preprocessed.

[0008] By adopting the above technical solution, a local knowledge graph is constructed based on the data source corresponding to the initial data to be filled. This facilitates the accurate identification of the graph location and influencing node parameters of the data, and makes it easier to clearly sort out the relationship logic and influence range between data. It avoids the processing target deviation caused by the chaotic data association and ambiguous influence relationship in conventional management. The importance level of data nodes is determined by the quantitative weighting of the graph location and influencing node parameters. The importance level of component nodes is clarified by combining the component level and the preset mapping relationship. This makes it easier to focus on core data while taking into account secondary data. The corresponding data processing strategy is matched based on the filling level, and the targeted data processing direction is determined by combining data quality characteristics. This forms a dual precise processing logic of level adaptation strategy and feature adaptation direction. It is easy to carry out differentiated preprocessing for data of different types, different importance levels, and different quality states, avoiding the problem of poor processing effect caused by the single data processing method and lack of targeting in conventional management.

[0009] In one possible implementation, the process of determining the underlying hidden danger data includes: Historical repair data corresponding to a preset observation period is obtained. Historical repair parameters of each part's process equipment are identified from the historical repair data. The historical repair parameters of each part's process equipment are quantified and normalized to obtain the historical repair value of each part's process equipment. The historical repair parameters include historical repair frequency, historical repair degree, and historical repair location. The equipment used for the operation of parts with historical repair values ​​higher than the preset repair threshold is identified as potentially hazardous parts and equipment. The process flow of the potentially hazardous parts and equipment is obtained from the process flow data of rail transit gearbox products. The process priority of the potentially hazardous parts and equipment is determined based on the process flow of the potentially hazardous parts and equipment. Obtain the content to be executed within a preset analysis time period, predict the simulated operation status parameters of the equipment with potential defects during the execution of the content to be executed based on the content to be executed, and identify the simulated defect features contained in the simulated operation status parameters based on the process priority of the equipment with potential defects. The underlying defect data is generated based on the historical repair data and simulated defect features of the defective parts and equipment.

[0010] By adopting the above technical solution, core parameters such as historical repair frequency, repair degree, and repair location are extracted from historical repair data and quantified and normalized. Abstract equipment repair information is transformed into quantifiable historical repair values, enabling precise screening of potentially hazardous parts and equipment. This avoids subjective biases or omissions caused by relying solely on manual experience to judge potentially hazardous equipment in conventional management. By analyzing the potential process flow of potentially hazardous parts and equipment to determine process priorities, it is easier to focus on the core links of gearbox process operations and prioritize high-priority potentially hazardous equipment that affects the operation of key parts and the realization of core functions. This avoids the lack of core risk control or waste of resources caused by indiscriminate handling of potential hazards. Finally, by integrating historical repair data and simulated defect characteristics to generate underlying potential hazard data, it not only retains the historical risk trajectory of potentially hazardous equipment but also incorporates potential defect information under future processing scenarios. This facilitates the provision of comprehensive and accurate potential hazard data support for the technical status management of rail transit gearbox products.

[0011] In one possible implementation, determining the process priority of the hazardous parts and equipment based on the hazardous process flow includes: The working part parameters corresponding to the equipment with the hidden danger are identified from the process flow of the hidden danger. The working part parameters include the hidden danger part and the relative part position of the hidden danger part in the rail transit gearbox. Obtain historical maintenance records of parts within a preset parts analysis time period, determine the maintenance frequency and maintenance level of the parts with potential problems within the preset parts analysis time period from the historical parts maintenance records, and determine the application importance level of the parts with potential problems based on the maintenance frequency and maintenance level of the parts with potential problems. Based on the relative position of the potentially hazardous parts in the rail transit gearbox, the corresponding part priority weight is determined, and the potential equipment process priority of the potentially hazardous parts is obtained by weighted summation based on the part priority and the application importance level.

[0012] By adopting the above technical solution, and by accurately extracting core parameters such as the location of potentially hazardous parts and their relative positions from the process flow, the correlation between potentially hazardous parts and the gearbox product structure is clarified. This avoids the control target deviation caused by the judgment of the priority of potential hazards being divorced from the actual structure of the product in conventional management. By combining the historical maintenance records of parts within a preset time period and quantifying the maintenance frequency and degree, the application importance level of the potentially hazardous parts is determined. The actual operation and maintenance risk of potentially hazardous parts is transformed into a quantifiable level indicator, which helps to avoid the one-sidedness of judging the importance of parts by relying solely on subjective experience. Through the weighted summation calculation of the priority weight of parts and the application importance level, it is easy to achieve the scientific quantification of the process priority of potentially hazardous equipment.

[0013] In one possible implementation, when the number of potential hazards in the defective component equipment exceeds a preset threshold, the method further includes: Based on the potential process flow of each potentially hazardous component or equipment, the associated process equipment for each potentially hazardous component or equipment is determined. Obtain the hazard performance parameters of each hazardous component and equipment at each historical analysis stage, and determine the fault migration degree between each hazardous component and equipment and the corresponding associated process equipment at each historical analysis stage based on the hazard performance parameters at each historical analysis stage. Obtain the operational performance parameters of each potentially hazardous component or equipment at each historical analysis stage, and determine the vibration amplitude and temperature change rate of each potentially hazardous component or equipment at each historical analysis stage based on the corresponding operational performance parameters. Based on the fault migration degree, vibration amplitude and temperature change rate of each potentially hazardous component in each historical analysis stage, the health observation value of each potentially hazardous component in each historical analysis stage is determined, and based on the health observation value of each potentially hazardous component in each historical analysis stage, the predicted health status value of each potentially hazardous component in the preset analysis time period is determined. The content to be executed for each hidden component or equipment during the prediction and analysis period is quantified into a predicted workload value. Based on the mapping relationship between the predicted workload value and the preset health value of each hidden component or equipment, the standard health value corresponding to each hidden component or equipment is determined, and the hidden component or equipment whose predicted health value is lower than the corresponding standard health value is identified as a component or equipment to be warned. The health difference between the predicted health status value and the standard health value of the part or equipment to be warned is determined, and the warning level is determined based on the health difference of the part or equipment to be warned and the process priority of the equipment with potential risks. A warning instruction is generated based on the warning level and the part or equipment to be warned.

[0014] By adopting the above technical solutions, and identifying the associated process equipment of potentially hazardous parts and equipment based on the hidden danger process flow, the collaborative linkage relationship between equipment is accurately sorted out. This breaks through the limitation of judging the hidden danger of a single piece of equipment in the isolation of conventional management. By combining the hidden danger performance parameters in the historical analysis stage to quantify the fault migration degree, and integrating the operation performance parameters to extract vibration amplitude and temperature change rate, a multi-dimensional data representation of the health status of potentially hazardous equipment is realized. This facilitates the avoidance of the one-sidedness of assessing the health status by a single indicator. Based on the fault migration degree, vibration amplitude, and temperature change rate, health observation values ​​are constructed, and the health status values ​​within a preset time period are predicted through time series analysis. This upgrades equipment health assessment from historical retrospection to future prediction. In addition, by determining the warning level based on the dual dimensions of health difference and the process priority of potentially hazardous equipment, the accuracy of the warning is improved. Timely warnings help avoid the risk of gearbox quality defects or production interruptions caused by the superposition of multiple hidden dangers, and further facilitate the manufacturing reliability and delivery stability of rail transit gearbox products.

[0015] In one possible implementation, the fault migration degree between the faulty component / equipment and the corresponding associated process equipment during the historical analysis phase is determined based on the fault manifestation parameters from the historical analysis phase, including: The inherent hazard status parameter value of the potentially hazardous parts and equipment is determined from the hazard performance parameters during the historical analysis phase. The inherent hazard status parameter value is determined by the hazard type and hazard level. Based on the self-hazard status parameter value, the association range is determined. Within the association range, the associated process equipment corresponding to the hazardous part equipment is identified. Based on the hazard performance parameters of the hazardous part equipment and the associated hazard performance parameters of the associated process equipment, the migration rate parameter is determined. The migration rate parameter includes the self-hazard migration rate and the associated hazard migration rate. The migration rate parameter is used to determine the fault migration degree between the defective component / equipment and the corresponding associated process equipment during the historical analysis phase.

[0016] By adopting the above technical solution, the abstract self-hazard status parameters are quantified into analyzable core indicators, and the correlation range is determined based on these indicators. This avoids the problem of the correlation range being too large or too small due to personal experience or fixed values ​​in conventional management, which can affect the identification accuracy of related process equipment. By combining the two-way hazard performance parameters of the hazardous parts and equipment and the related process equipment, the migration rate parameters of the self and the related processes are quantified. This allows for a comprehensive reflection of the speed characteristics of hazard transmission, avoiding the one-sidedness of assessing migration risk from a single dimension. As a result, it is easier to achieve the scientific and accurate quantification of fault migration.

[0017] In one possible implementation, after determining the visualization technology status management table, the following is also included: Identify feedback nodes from the user task instructions and obtain the acceptable display type corresponding to the feedback nodes; Based on the acceptable display type and the visualization technology status management table, feedback data is determined and fed back to the feedback node.

[0018] By adopting the above technical solution, feedback data is generated by identifying and optimizing the visualization technology status management table based on acceptable display types. This ensures that the feedback data originates from a unified technology status data base and is tailored to the actual usage scenario of the feedback node, thus avoiding duplicate data transmission or redundant invalid information.

[0019] Secondly, this application provides a management system, which adopts the following technical solution: A management system comprising: At least one processor; Memory; At least one application, wherein the at least one application is stored in memory and configured to be executed by at least one processor, the at least one application being configured to: execute the above-described rail transit gearbox product technical status management method.

[0020] Thirdly, this application provides a computer-readable storage medium, which adopts the following technical solution: A computer-readable storage medium includes: a computer program stored thereon that can be loaded by a processor and execute the above-described method for managing the technical status of rail transit gearbox products.

[0021] Fourthly, this application provides a computer program product, which adopts the following technical solution: A computer program product includes a computer program that, when executed by a processor, implements the aforementioned method for managing the technical status of rail transit gearbox products.

[0022] In summary, this application includes at least one of the following beneficial technical effects: Based on preset technical status management rules, baseline tasks are created, breaking down gearbox technical status management into a clear and implementable task framework. This ensures that all technical status data is collected around a unified task objective, avoiding management chaos caused by scattered data, overlapping tasks, and unclear responsibilities in conventional management. It guarantees the standardization of data management from the source. In addition, targeted data preprocessing is performed based on the data type of the initial data to be filled, which helps to avoid decision-making biases caused by manual entry errors, chaotic data formats, and invalid data interference in conventional management. This ensures the reliability of management results from the perspective of data quality. Automatic data association is achieved through task identification without manual intervention, thereby improving the efficiency and accuracy of data collection. Finally, by integrating the target data to be filled into a visual status management table, relevant core content can be directly presented to users, enabling them to quickly obtain the information they need and make corresponding decisions.

[0023] A local knowledge graph is constructed based on the data sources corresponding to the initial data to be filled. This facilitates the accurate identification of the graph location and influencing node parameters of the data, and makes it easier to clearly understand the relationship logic and scope of influence between data. This avoids the processing targeting deviation caused by chaotic data associations and ambiguous influence relationships in conventional management. The importance level of data nodes is determined by quantitative weighting of graph location and influencing node parameters. The importance level of component nodes is clarified by combining component levels and preset mapping relationships. This makes it easier to prioritize core data while taking into account secondary data. The corresponding data processing strategy is matched based on the filling level. The targeted data processing direction is determined by combining data quality characteristics. This forms a dual precise processing logic of level adaptation strategy and feature adaptation direction. This makes it easier to implement differentiated preprocessing for data of different types, importance levels and quality states. This avoids the problem of poor processing effect caused by the single data processing method and lack of targeting in conventional management. Attached Figure Description

[0024] Figure 1 This is a flowchart illustrating a method for managing the technical status of a rail transit gearbox product according to an embodiment of this application. Figure 2 This is a diagram illustrating a data format rule in an embodiment of this application; Figure 3 This is a template illustration of a product technical status management table in one embodiment of this application; Figure 4 This is a modified template illustration from an embodiment of this application; Figure 5 This is a new customer gearbox information interface in an embodiment of this application; Figure 6 This is a schematic diagram of the structure of a management system according to an embodiment of this application. Detailed Implementation

[0025] The following is in conjunction with the appendix Figures 1 to 6 This application will be described in further detail.

[0026] After reading this specification, those skilled in the art may make modifications to this embodiment without contributing any inventive step, but such modifications are protected by patent law as long as they fall within the scope of the claims of this application.

[0027] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0028] It should be noted that, in the optional embodiments of this application, the data related to object information, when applied to specific products or technologies, requires the permission or consent of the object. Furthermore, the collection, use, and processing of this data must comply with the relevant laws, regulations, and standards of the relevant countries and regions. In other words, if the embodiments of this application involve data related to an object, it must be obtained with the object's authorization and consent, the authorization and consent of relevant departments, and in accordance with the relevant laws, regulations, and standards of the country and region. If the embodiments involve personal information, the acquisition of all personal information requires the individual's consent. If sensitive information is involved, the separate consent of the information subject is required. The embodiments also need to be implemented with the object's authorization and consent.

[0029] Specifically, this application provides a method for managing the technical status of rail transit gearbox products, executed by a management system. This management system can be a server or a terminal device. The server can be an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services. The terminal device can be a smartphone, tablet, laptop, desktop computer, etc., but is not limited to these. The terminal device and the server can be directly or indirectly connected via wired or wireless communication, and this application does not impose any limitations on this.

[0030] refer to Figure 1 , Figure 1 This is a flowchart illustrating a method for managing the technical status of a rail transit gearbox product according to an embodiment of this application. The method includes steps S110-S140, wherein: Step S110: Create a baseline task for the technical status management of rail transit gearbox products based on preset technical status management rules.

[0031] Specifically, preset technical status management rules are standardized management specifications set by relevant technical personnel based on actual needs and relevant industry standards. These rules can be determined by the relevant technical personnel and then uploaded to the management system. Preset technical status management rules may include: a. Identifier and version management rules: The gearbox part number adopts an 8-digit numeric encoding format, for example, "B00000002" (the first 2 digits are the product category code, and the last 6 digits are the serial number); Assign a unique code according to the configuration type. For example, the process operation instruction number is “PNT_WIN_000002” (the prefix “PNT_WIN_” is the process type identifier, and the last 6 digits are the serial number). Version information is identified by a letter sequence, with the initial version being "AA". Each valid change increments the letter sequence (e.g., AA→AB→AC…→AZ→BA…), clearly recording the data update and iteration trajectory. b. Configuration information type definition management rules: Document data can include design documents, customer technical agreements, process or quality design documents, and documents received from external platforms, including formats such as .doc, .xlsx, and .ppt. Two-dimensional drawings and three-dimensional models of products, including formats such as .dwg, .stp, .igs, and .asm; Product PBOM (Product Bill of Materials) / MBOM (Manufacturing Bill of Materials); c. Configuration information data format management rules: The default data formats for the configuration information include, but are not limited to, .stp, .pptx, .flv, .zip, etc. Figure 2 As shown; d. Preset the product technical status management table template to be exported: Template content includes, but is not limited to, product name, part number, project name, customer name, baseline version, etc. Figure 3 As shown; e. Attribute options preset management rules: This includes, but is not limited to, data such as customer name, specifications, and project name, which are used for system identification and accurate matching during data transmission. When establishing baseline tasks or transmitting data to external platforms, users only need to select from the system options and do not need to manually enter the data. f. Preset change template: The template content includes, but is not limited to, name, applicable products, changes, pre-change instructions, and post-change instructions, such as... Figure 4 As shown; g. Document data management rules: Visualization requirements: Document name, number, creator, department, document type, initial version, current version, and revised version number should be displayed visually. Document control requirements: All documents under the target baseline task must be approved, in a release state, and under control. Change tracking requirements: From the establishment of the target baseline task to the present moment, the technical change requests related to the referenced documents can be displayed via links; Access control: Document data can only be visualized; downloading data sources or other operations require clicking on the name or number to access the data. h.BOM (Bill of Materials) data management rules: The technical status management scope of the gearbox BOM structure is limited to the first level (i.e., the direct lower-level materials corresponding to the gearbox as a whole, such as planetary gear assemblies, housings, high-speed shaft assemblies, etc.). Lower-level sub-BOMs can be viewed through hierarchical association, ensuring that management focuses on the core material structure. i. Management rules for product 2D / 3D drawings and models: Product technical status management for 2D / 3D drawings and models of gearboxes: The technical status management module displays basic information such as model / drawing name and number, and allows users to click on related objects to enter the design view for visual browsing; it does not include operation functions for 3D models / 2D drawings. j. Management rules for receiving data from external affiliated platforms: External related systems include quality management systems, procurement management systems, and market information. After external data is transmitted, it is uniformly managed based on the management system for technical status, including but not limited to the need for consistent customer names, specifications, and project names; data from external platforms is transmitted in document formats recognizable by the management system; acceptance documents issued by superiors, special inspection requirements from customers, etc.; technical agreements signed with external suppliers or outsourcing parties of key components, technical response status based on products, and change requests submitted by external suppliers / outsourcing parties of important components, etc.

[0032] Based on preset technical status management rules, a baseline task for technical status management is created. That is, using these rules as the core basis, the technical status management objectives for rail transit gearbox products are broken down into implementable, traceable, and quantifiable structured task units. The core elements and execution requirements of each task are clearly defined, forming a baseline framework for technical status management. The specific operation process involves, after identity verification, determining the technical status item (i.e., the target gearbox product), then determining the configuration information required for the target technical status item. Clicking "New" leads to the "Create Customer Gearbox Information" interface, where the customer name, product name, product drawing number, gearbox number, etc., are entered. Figure 5 As shown, once determined, a technical status baseline task will be established in the management system. The baseline task in this application can be understood as a data container with standardized rule constraints.

[0033] Step S120: Identify the task identifier contained in the baseline task, and identify multiple initial data to be filled corresponding to the baseline task from the preset multi-source heterogeneous database based on the task identifier. The preset multi-source heterogeneous database contains top-level original core data, mid-level collaborative data and bottom-level hidden danger data.

[0034] Specifically, task identifiers can be identified from the baseline task based on a preset feature recognition algorithm. Then, relevant content is extracted from a preset multi-source heterogeneous database based on the task identifiers to fill the unfilled areas in the baseline task. The specific preset feature recognition algorithm is not limited in this embodiment. Since the baseline task in this application can be considered a data container with standardized rule constraints, it can be understood that the baseline task contains multiple unfilled areas, each with a different task identifier. After extracting the corresponding initial unfilled area data based on the task identifiers of each unfilled area, the initial unfilled data is filled into the corresponding unfilled area to complete the data filling operation. The preset multi-source heterogeneous database includes, but is not limited to, top-level native core data, mid-level collaborative data, and bottom-level hidden danger data from different data sources, wherein: Top-level native core data refers to the native core data of the management system, which is the top-level configuration data directly associated with the gearbox in the management system. This includes, but is not limited to, the product MBOM (Manufacturing Bill of Materials), EBOM (Engineering Bill of Materials), PBOM (Process Bill of Materials), product 2D / 3D drawings, design schemes, calculation reports, customer technical agreements, process / quality documents, and initiated change requests, verification forms, etc. It is the basic data source for technical status management. Cross-platform collaborative data comes from external related platforms related to gearbox R&D, procurement, and quality control, including but not limited to acceptance documents and special customer inspection requirements issued by the quality management system; supplier technical agreements and outsourcing change applications transmitted by the procurement management system; personalized customer needs and project management requirements synchronized by the market information system; and gearbox operating condition data collected by the vehicle monitoring system, etc., to achieve cross-link data interconnection and interoperability. The underlying hidden danger data is early warning data that focuses on potential risks throughout the entire life cycle of the gearbox. It is the core data source that supports the proactive prediction of technical status. It mainly includes, but is not limited to, historical traceability data, real-time monitoring data and simulation prediction data related to equipment failure, quality defects and process deviations in the entire process of gearbox R&D, production and operation and maintenance. It is used to identify potential risks in advance, locate the root cause of hidden dangers and avoid the expansion of failures or batch quality problems.

[0035] Step S130: Identify the data source, part level, and data quality characteristics corresponding to each initial data to be filled, and perform data preprocessing on each initial data to be filled based on the data source, part level, and data quality characteristics corresponding to each initial data to be filled to obtain the target data to be filled.

[0036] Specifically, for any initial data to be filled, a preset feature recognition algorithm can be used to identify the data source, component level, and data quality characteristics corresponding to the initial data to be filled. The data source represents the core information of the origin of the initial data to be filled, the business process to which it belongs, and the storage platform. Its core function is to clarify the source of the data, provide a basis for subsequent data preprocessing, and avoid association errors caused by ambiguous data sources. The data source can be divided into business process sources, such as design, production, testing, and operation and maintenance, and data storage space sources, such as internal systems, external systems, and third-party platforms. These can be selected and set according to actual needs. The component level is the importance level of the component related to the initial data to be filled to the core function of the rail transit gearbox. It can be determined by identifying the component codes contained in the initial data to be filled. The data quality characteristics are used to evaluate the quality of the initial data to be filled. They can be determined by identifying the data completeness, data timeliness, and cross-platform data consistency of the initial data to be filled. The specific determination method is not specifically limited in this embodiment. After determining the data source, part level, and data quality characteristics of the initial data to be filled, a targeted data preprocessing method can be selected for the initial data to be filled to obtain the target data to be filled.

[0037] Step S140: When a user task instruction is detected, identify the task decision identifier from the user task instruction, and determine the visualization technology status management table based on the task decision identifier and the target data to be filled.

[0038] Specifically, user task instructions can be transmitted to the management system by the relevant user through a terminal device, or input by directly accessing or logging into the relevant task distribution interface in the management system. The specific implementation method is not limited in this embodiment. A task decision identifier can be identified from the user task instructions based on a preset feature recognition algorithm. The task decision identifier represents the access needs of the relevant user. After extracting relevant core data from the target data to be filled based on the task decision identifier, the relevant core data can be directly projected onto the corresponding baseline task bar, that is, the relevant core data is filled into the corresponding area to be filled, resulting in a visual technology status management table.

[0039] In this embodiment, a baseline task is created based on preset technical status management rules, breaking down gearbox technical status management into a clear and implementable task framework. This ensures that all technical status data are collected around a unified task objective, avoiding management chaos caused by data dispersion, task overlap, and unclear responsibilities in conventional management. This guarantees the standardization of data management from the source. In addition, targeted data preprocessing is performed based on the data type of the initial data to be filled, which helps avoid decision-making biases caused by manual entry errors, chaotic data formats, and invalid data interference in conventional management. This ensures the reliability of management results from the perspective of data quality. Automatic data association is achieved through task identification without manual intervention, thereby improving the efficiency and accuracy of data collection. Finally, by integrating the target data to be filled into a visual status management table, relevant core content can be directly presented to users, enabling them to quickly obtain the required information and make corresponding decisions.

[0040] Furthermore, to improve the data preprocessing effect, the process of preprocessing the initial data to be filled based on the data source, part level, and data quality characteristics can include: A local knowledge graph is constructed based on the data source corresponding to each initial data to be filled. The graph location and influencing node parameters of the initial data to be filled are identified from the local knowledge graph. These parameters include the number and type of influencing nodes. The graph location and influencing node parameters are then quantitatively weighted to determine the importance level of the data nodes. Based on the mapping relationship between the part level and the preset part node importance level, the importance level of the part nodes corresponding to the part level is determined. The filling level of the initial data to be filled is determined based on the importance level of the data nodes and the importance level of the part nodes. Based on the mapping relationship between the filling level and the preset processing strategy, the data processing strategy for the initial data to be filled is determined. Based on the mapping relationship between the data quality characteristics of the initial data to be filled and the preset processing direction, the data processing direction for the initial data to be filled is determined. The preset processing direction mapping relationship is the correspondence between the data quality characteristics and the data processing direction. Based on the data processing strategy and data processing direction, the initial data to be filled is preprocessed.

[0041] Specifically, by constructing a local knowledge graph, the relationships between all initial data to be populated can be analyzed intuitively. When constructing a local knowledge graph, each initial data to be populated can be regarded as a data node. Then, based on the data source corresponding to each initial data to be populated, the source label of each data node is determined. Based on each source label and the preset business process, the node connection logic between each data node is determined. Based on the node connection logic between each data node, a local knowledge graph corresponding to all initial data to be populated is constructed. The graph position and influencing node parameters corresponding to all initial data to be populated can be determined based on this local knowledge graph.

[0042] The graph position corresponding to the initial data to be filled is used to characterize the hierarchical status and association range of the data node corresponding to the initial data to be filled in the local knowledge graph, and can be represented by graph hierarchy. The influencing node parameter includes the number and type of influencing nodes corresponding to the data node. Among them, nodes in the local knowledge graph that have a direct relationship with the data node can be identified as influencing nodes. The influencing node type includes parent nodes and child nodes. The influence weight corresponding to the graph position can be determined first based on the preset influence weight mapping relationship. Then, the graph position, the number of influencing nodes, and the proportion of influencing node types are quantified, and the quantification results of the three are weighted and calculated based on the influence weight to determine the importance level of the data node corresponding to the initial data to be filled. The influence weight is the weight among the graph position, the number of influencing nodes, and the proportion of influencing node types. The preset influence weight mapping relationship is the correspondence between the graph position and the influence weight. The specific content can be determined by relevant personnel based on historical experimental data and uploaded to the management system. The specific content is not specifically limited in this embodiment of the application.

[0043] The component hierarchy can reflect the core status of the corresponding component in the overall structure of the rail transit gearbox. The core status of the component hierarchy can be quantified based on the preset component node importance level mapping relationship to obtain the component node importance level of the component hierarchy. The higher the core status, the higher the corresponding component node importance level. The specific content of the preset component node importance level mapping relationship is not specifically limited in this application embodiment, but can be determined by relevant personnel based on historical experimental data and then uploaded to the management system.

[0044] After determining the importance levels of data nodes and component nodes, the sum of these two levels can be used to calculate the initial fill level corresponding to the data to be filled. Different fill levels correspond to different data processing strategies, which can be determined based on a preset processing strategy mapping relationship. This preset processing strategy mapping relationship is the correspondence between fill levels and data processing strategies. For example, when the fill level is level a, the corresponding data processing strategy is to use a high-precision feature extraction algorithm; when the fill level is level b, the corresponding data processing strategy is to use a standard feature extraction algorithm; and when the fill level is level c, the corresponding data processing strategy is to use a lightweight feature extraction algorithm. Among these, level a is higher than level b, and level b is higher than level c.

[0045] The data processing direction is related to data quality characteristics. It can be determined based on the data completeness, timeliness, and cross-platform data consistency of the initial data to be filled, as well as a preset processing direction mapping relationship. For example, when data completeness ≥ 95%, data timeliness ≤ 24h, and cross-platform data consistency ≥ 95%, the corresponding processing direction is feature enhancement extraction + collaborative verification of related data; when 90% ≤ data completeness < 95%, 24h < data timeliness ≤ 72h, and 90% ≤ cross-platform data consistency < 95%, the corresponding processing direction is related data completion + deviation correction + feature filtering; when data completeness < 90%, data timeliness > 72h, and cross-platform data consistency < 90%, the corresponding processing direction is cross-source cross-validation repair + invalid data removal + manual review triggering. The specific content of the preset processing direction mapping relationship is not specifically limited in this embodiment and can be determined by relevant technical personnel based on historical experimental data and then uploaded to the management system.

[0046] Finally, targeted preprocessing operations are performed based on the data processing strategies and directions corresponding to each initial data to be filled, which helps to avoid the problem of poor processing results caused by the single and untargeted data processing methods in conventional management.

[0047] Furthermore, to facilitate the provision of comprehensive and accurate hidden danger data support for the technical status management of rail transit gearbox products, this application embodiment provides a specific process for determining the underlying hidden danger data, including: Historical repair data corresponding to a preset observation period is acquired. Historical repair parameters for each component's process equipment are identified from this data, and these parameters are quantified and normalized to obtain historical repair values. These parameters include historical repair frequency, degree of repair, and location. Process equipment with historical repair values ​​exceeding a preset repair threshold is identified as potentially hazardous components. The hazardous process flow for these components is obtained from the process flow data of rail transit gearbox products. Based on this process flow, the hazardous process priority for these components is determined. Executable content within a preset analysis period is acquired. Based on this content, simulated operational status parameters of the hazardous components during execution are predicted. Simulated defect features within these simulated operational status parameters are identified based on the hazardous process priority. Finally, underlying hazardous data is generated based on the historical repair data and simulated defect features of the hazardous components.

[0048] Specifically, the preset observation period is a period of time prior to the current moment. The duration of the preset observation period can be 3 days or 5 days, and the specific duration is not specifically limited in this embodiment. The historical repair data includes the historical repair status of each part's process equipment within the preset observation period. The part's process equipment refers to equipment used for subsequent processes such as dimensional measurement, inspection, cleaning, assembly, painting, and testing of already processed parts. By analyzing the historical repair status of each part's process equipment, it is convenient to analyze and summarize potential operational hazards that parts may face during subsequent processes. Based on a preset feature recognition algorithm, the historical repair frequency, historical repair degree, and historical repair location of each part's process equipment within a preset observation period can be identified from historical repair data. Then, the historical repair frequency, historical repair degree, and historical repair location are quantified and normalized to obtain the historical repair value for each part's process equipment. A higher historical repair frequency corresponds to a higher historical repair value. The historical repair degree characterizes the ratio between the repaired area of ​​the part's process equipment and the overall equipment structure; a higher historical repair degree corresponds to a higher historical repair value. The historical repair location is the center of the repaired area; the closer the center of the area is to the core of the part's process equipment, the higher the corresponding historical repair value. After determining the historical repair value for each part's process equipment using the above method, each historical repair value can be compared with a preset repair threshold. Parts with historical repair values ​​higher than the preset repair threshold are identified as potentially hazardous parts. The number of potentially hazardous parts and the specific values ​​corresponding to the preset repair thresholds are not specifically limited in this embodiment.

[0049] The management system stores process flow data for rail transit gearbox products, specifically the process flow of each component's equipment. Based on equipment codes, the system can extract the potential process flow of hazardous components from this data and determine the corresponding technological priority of the hazardous equipment. In this embodiment, the technological priority of hazardous equipment refers to the priority at the component level, i.e., the importance of the component in the hazardous process flow within the rail transit gearbox. Furthermore, to facilitate the scientific quantification of the technological priority of hazardous equipment, the method provided in this embodiment, when determining the technological priority of hazardous components based on the hazardous process flow, may specifically include: The process involves identifying the operational parameters of the potentially hazardous parts and equipment from the process flow. These parameters include the hazardous parts themselves and their relative positions within the rail transit gearbox. Historical maintenance records for the parts are acquired within a preset analysis period. These records determine the maintenance frequency and severity of the hazardous parts within this period, and the application importance level is determined based on these records. Finally, based on the relative positions of the hazardous parts within the rail transit gearbox, a corresponding priority weight is determined. A weighted summation of the priority and application importance level is then performed to calculate the process priority of the potentially hazardous equipment.

[0050] Specifically, based on a preset feature recognition algorithm, the potential hazards can be identified from the hidden danger process flow, including the corresponding hidden danger parts and their relative positions in the rail transit gearbox. This is achieved by analyzing the maintenance records of the hidden danger parts over a historical period. The preset analysis period is a time preceding the current moment, primarily referring to the application stage of the hidden danger part. The duration of this preset analysis period can be 30 days or 50 days; the specific duration is not specifically limited in this embodiment. Historical maintenance records can be comprised of maintenance requests submitted to the management system by relevant users during actual use. Higher maintenance frequency and higher maintenance level correspond to higher application importance. The maintenance level is the proportion of the hidden danger part's maintenance scope to its overall structure, and can be determined based on a preset application importance level mapping relationship. This preset application importance level mapping relationship is the correspondence between the combination parameters of maintenance frequency and maintenance level and the application importance level; the specific details are not specifically limited in this embodiment. The smaller the distance between the relative position of the potentially hazardous component in the rail transit gearbox and the center point of the gearbox, the higher the priority weight of the component. Finally, a weighted summation is performed based on the component priority and application importance level to calculate the process priority of the potentially hazardous component. By combining historical component maintenance records within a preset time period and quantifying the maintenance frequency and degree, the application importance level of the processed component is determined. This transforms the actual maintenance risk of the component into a quantifiable level indicator, avoiding the one-sidedness of relying solely on subjective experience to determine the importance of the component.

[0051] The preset analysis time period is a period of time after the current moment. The duration of the preset analysis time period can be 12 hours or 24 hours, and the specific duration is not specifically limited in this embodiment. The content to be executed during the preset analysis time period is the relevant process operation that the potentially hazardous part equipment needs to perform within the preset analysis time period. This can be achieved by constructing a simulation model of the potentially hazardous part equipment at the current moment and inputting the content to be executed into the simulation model to obtain the simulated operation status parameters of the potentially hazardous part equipment when simulating the execution of the content. The simulated operation status parameters include the surface contour deviation, critical dimension deviation, and geometric tolerance deviation of the potentially hazardous part. The hazard judgment criteria are determined according to the process priority of the potentially hazardous equipment. Based on the hazard judgment criteria, the presence of simulated defect features in the simulated operation status parameters is identified and judged. For example, the simulated defect feature is the surface contour deviation of the part that is greater than the preset contour deviation value.

[0052] Historical repair data is analyzed from the perspective of potentially hazardous parts and equipment, while simulated defect characteristics are analyzed from the perspective of potentially hazardous parts. By integrating historical repair data of potentially hazardous parts and equipment with simulated defect characteristics of potentially hazardous parts, underlying potential data is generated. This not only preserves the historical risk trajectory of potentially hazardous parts and equipment but also incorporates potential defect information that parts and components may face in future operating scenarios. This facilitates the provision of comprehensive and accurate potential data support for the technical status management of rail transit gearbox products.

[0053] When the number of potential defects in the defective parts exceeds a preset threshold, the method provided in this application embodiment may further include: Based on the problematic process flow of each hazardous component / equipment, the associated process equipment for each hazardous component / equipment is identified; the hazardous performance parameters of each hazardous component / equipment at each historical analysis stage are obtained, and the fault migration degree between each hazardous component / equipment and its corresponding associated process equipment at each historical analysis stage is determined based on these parameters; the operational performance parameters of each hazardous component / equipment at each historical analysis stage are obtained, and the vibration amplitude and temperature change rate corresponding to each hazardous component / equipment at each historical analysis stage are determined based on these parameters; based on the fault migration degree, vibration amplitude, and temperature change rate corresponding to each hazardous component / equipment at each historical analysis stage, the corresponding health observation values ​​for each hazardous component / equipment at each historical analysis stage are determined, and... Based on the health observation values ​​of each potentially hazardous component / equipment at each historical analysis stage, the predicted health status value of each potentially hazardous component / equipment for the preset analysis time period is determined. The tasks to be performed by each potentially hazardous component / equipment for the predicted analysis time period are quantified into predicted workload values. Based on the mapping relationship between the predicted workload values ​​and preset health values ​​of each potentially hazardous component / equipment, the standard health value corresponding to each potentially hazardous component / equipment is determined. Components / equipment with predicted health status values ​​lower than the corresponding standard health values ​​are identified as components / equipment requiring early warning. The health difference between the predicted health status value and the standard health value of the component / equipment requiring early warning is determined. Based on the health difference and the process priority of the potentially hazardous component / equipment, an early warning instruction is generated.

[0054] Specifically, the preset quantity threshold can be 3 or 5, and the specific value is not specifically limited in this embodiment. The associated process operation equipment refers to the upstream and downstream equipment of the potentially hazardous parts equipment, which can be identified from the process flow objects in the potentially hazardous process flow based on a preset feature recognition algorithm. Further analysis is conducted on the stage performance of each potentially hazardous parts equipment in each historical analysis stage. Each historical analysis stage corresponds to the same duration, and each historical analysis stage is an adjacent time period before the current moment. In addition to analyzing its own potential hazards, the impact of its own potential hazards on associated process operation equipment can also be analyzed to determine the fault migration degree of the potentially hazardous parts equipment in each historical analysis stage. Furthermore, to improve the accuracy of determining the fault migration degree of the potentially hazardous parts equipment, the fault migration degree between the potentially hazardous parts equipment and the corresponding associated process operation equipment in the historical analysis stage is determined based on the potential hazard performance parameters of the historical analysis stage. Specifically, this may include: The inherent hazard status parameter values ​​of the potentially hazardous parts and equipment are determined from the hazard performance parameters during the historical analysis phase. These parameters are determined by the hazard type and hazard level. Based on these inherent hazard status parameter values, the associated range is determined. Within this range, the associated process equipment corresponding to the potentially hazardous parts and equipment is identified. Based on the hazard performance parameters of the potentially hazardous parts and equipment and the associated hazard performance parameters of the associated process equipment, the migration rate parameters are determined. These parameters include the migration rate of the inherent hazard and the migration rate of the associated hazard. Based on these migration rate parameters, the fault migration degree between the potentially hazardous parts and equipment and the corresponding associated processing equipment during the historical analysis phase is determined.

[0055] Specifically, the inherent hazard status parameters of the potentially hazardous parts and equipment can be determined based on historical repair parameters. These parameters mainly include the hazard type and hazard level. Hazard types may include, but are not limited to, equipment wear, equipment cracks, and loose equipment fits. The hazard level can be determined based on the hazard type and severity, as different hazard types and severity levels correspond to different hazard levels. This can be determined based on a preset hazard level mapping relationship, which is not specifically limited in this embodiment. The larger the inherent hazard status parameter value, the greater the corresponding diffusion distance. This distance can be determined based on a preset diffusion distance mapping relationship. Then, with the location of the potentially hazardous parts and equipment as the center, the diffusion distance is used to obtain the associated range. The preset diffusion distance mapping relationship is the correspondence between the inherent hazard status parameter value and the diffusion distance. The upstream and downstream equipment of the potentially hazardous parts and equipment are located within the associated range and identified as associated process operation equipment.

[0056] When determining the migration rate parameter based on the hazard performance parameters of defective parts and related process equipment, the hazard type can be identified from the hazard performance parameters first. Then, the corresponding investigation features of the related hazard performance parameters can be determined according to different hazard types. For example, when the hazard type is equipment wear, the investigation feature for upstream equipment is feature a, and the investigation feature for downstream equipment is feature b. Feature a is the cause of equipment wear in the defective parts, feature b is the impact of equipment wear on downstream equipment, the related hazard migration rate is the rate at which feature a induces equipment wear in the defective parts, and the hazard migration rate is the rate at which equipment wear on downstream equipment is affected.

[0057] Based on a pre-defined migration mapping relationship, the migration rates of the defective component and related defects are quantified and summed to obtain the fault migration degree between the defective component / equipment and its corresponding related process equipment during the historical analysis phase. By combining the bidirectional defect performance parameters of the defective component / equipment and its related process equipment, the migration rate parameters of both the defective component / equipment and its related processes are quantified, which facilitates a comprehensive reflection of the speed characteristics of defect propagation and avoids the one-sidedness of assessing migration risk from a single dimension. Based on the above method, the fault migration degree corresponding to each defective component / equipment can be determined.

[0058] The operational performance parameters of the potentially hazardous parts and equipment are the actual operational status during the historical analysis phase. Based on a preset feature recognition algorithm, the vibration amplitude and temperature change rate of each potentially hazardous part and equipment in each historical analysis phase can be identified from the operational performance parameters of each historical analysis phase. Then, the fault migration degree, vibration amplitude, and temperature change rate of the potentially hazardous parts and equipment in each historical analysis phase are comprehensively analyzed, and the health observation value of each potentially hazardous part and equipment in each historical analysis phase is obtained after quantification and summation. The system can fit the health observation values ​​of each historical analysis stage using a preset linear regression fitting algorithm to obtain the health decay slope corresponding to each potentially hazardous component or equipment, and determine the decay ratio between the corresponding health decay rates of adjacent historical analysis stages, thus determining the health decay coefficient of each potentially hazardous component or equipment. Based on the health observation values ​​of each potentially hazardous component or equipment in each historical analysis stage, the system can determine the health standard deviation of each potentially hazardous component or equipment in each historical analysis stage. Using a preset sliding window interpolation method, the health observation values ​​of each potentially hazardous component or equipment in each historical analysis stage, and a preset window threshold, the system can determine the health mutation parameters of each potentially hazardous component or equipment, including the number of mutation nodes and the proportion of mutation types. Finally, the system inputs the health decay slope, health decay coefficient, health standard deviation, and health mutation parameters of each potentially hazardous component or equipment into a preset time-series prediction network model to obtain the predicted health status value of each potentially hazardous component or equipment within a preset analysis time period.

[0059] Based on a preset feature recognition algorithm, the system can identify workload characteristics from the tasks to be executed within the predicted analysis period and quantify these characteristics into predicted workload values. Workload characteristics can include task duration, task difficulty, etc. Then, based on the mapping relationship between the predicted workload values ​​and preset health values ​​of potentially hazardous parts and equipment, the system determines the standard health value required for these parts and equipment to complete the corresponding tasks. The system compares the standard health value of each potentially hazardous part and equipment with its predicted health status value to identify parts and equipment requiring early warning. These parts and equipment are highly prone to failure when executing the corresponding tasks within the predicted analysis period. Once identified, early warning commands can be generated directly. Alternatively, the system can first determine the health difference between the predicted health status value and the standard health value of the parts and equipment requiring early warning. Then, the warning level is determined based on the health difference and the process priority of the potentially hazardous equipment. A larger health difference and a higher process priority correspond to a higher warning level. Finally, an early warning command is generated based on the warning level and the parts and equipment requiring early warning. This improves the accuracy of early warnings and helps avoid the risk of gearbox quality defects or production interruptions caused by multiple overlapping hazards.

[0060] Furthermore, the method provided in this application, after determining the visualization technology status management table, may further include: Identify feedback nodes from user task instructions and obtain the acceptable display type corresponding to the feedback node; based on the acceptable display type and the visualization technology status management table, determine the feedback data and feed the feedback data back to the feedback node.

[0061] Specifically, user task instructions can be sent to the management system by relevant users through terminal devices. Feedback nodes can be regarded as feedback terminals. Different feedback nodes have different acceptable display types, such as documents, tables, and images. For example, after generating a visual technology status management table, relevant users can manually trigger the ERP interface. Based on the acceptable display type corresponding to the ERP interface, the interface fields of the visual technology status management table are transformed to obtain feedback data, which is then sent to the ERP system. By identifying and optimizing the visual technology status management table based on the acceptable display type, feedback data can be generated, ensuring that the feedback data originates from a unified technical status data base and fits the actual usage scenario of the feedback node, thus avoiding duplicate data transmission or redundant invalid information.

[0062] This application provides a management system, such as... Figure 6 As shown, Figure 6The management system 600 shown includes a processor 601 and a memory 603. The processor 601 and the memory 603 are connected, for example, via a bus 602. Optionally, the management system 600 may also include a transceiver 604. It should be noted that in practical applications, the transceiver 604 is not limited to one, and the structure of this management system 600 does not constitute a limitation on the embodiments of this application.

[0063] Processor 601 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. Processor 601 may also be a combination that implements computational functions, such as including one or more microprocessor combinations, a combination of a DSP and a microprocessor, etc.

[0064] Bus 602 may include a pathway for transmitting information between the aforementioned components. Bus 602 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. Bus 602 can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 6 The symbol is represented by only one line, but this does not mean that there is only one bus or one type of bus.

[0065] The memory 603 may be a ROM (Read Only Memory) or other type of static storage device capable of storing static information and instructions, RAM (Random Access Memory) or other type of dynamic storage device capable of storing information and instructions, or an EEPROM (Electrically Erasable Programmable Read Only Memory), CD-ROM (Compact Disc Read Only Memory) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto.

[0066] The memory 603 stores application code that executes the scheme of this application, and its execution is controlled by the processor 601. The processor 601 executes the application code stored in the memory 603 to implement the content shown in the foregoing method embodiments.

[0067] The management system includes, but is not limited to, mobile terminals such as mobile phones, laptops, digital radio receivers, PDAs (Personal Digital Assistants), PADs (Tablet PCs), PMPs (Portable Multimedia Players), and in-vehicle terminals (e.g., in-vehicle navigation terminals), as well as fixed terminals such as digital TVs and desktop computers. It can also include servers. Figure 6 The management system shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0068] This application provides a computer-readable storage medium storing a computer program that, when run on a computer, enables the computer to execute the corresponding content in the aforementioned method embodiments.

[0069] This application provides a computer program product including a computer program that, when executed by a processor, implements the methods described in any of the above embodiments.

[0070] It should be understood that although the steps in the flowcharts of the accompanying figures are shown sequentially as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the accompanying figures may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the sub-steps or stages of other steps.

[0071] The above description is only a partial embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A method for managing the technical status of rail transit gearbox products, characterized in that, include: Based on preset technical status management rules, create baseline tasks for the technical status management of rail transit gearbox products; Identify the task identifier contained in the baseline task, and identify multiple initial data to be filled corresponding to the baseline task from a preset multi-source heterogeneous database based on the task identifier. The preset multi-source heterogeneous database contains top-level native core data, mid-level collaborative data, and bottom-level potential data. Identify the data source, part level, and data quality characteristics corresponding to each initial data to be filled, and perform data preprocessing on each initial data to be filled based on the data source, part level, and data quality characteristics corresponding to each initial data to be filled to obtain the target data to be filled; When a user task instruction is detected, a task decision identifier is identified from the user task instruction, and a visualization technology status management table is determined based on the task decision identifier and the target data to be filled.

2. The method for managing the technical status of rail transit gearbox products according to claim 1, characterized in that, Based on the data source, part level, and data quality characteristics corresponding to the initial data to be filled, the initial data to be filled is preprocessed, including: A corresponding local knowledge graph is constructed based on the data source corresponding to each initial data to be filled, and the graph position and influencing node parameters of the initial data to be filled are identified from the local knowledge graph. The influencing node parameters include the number of influencing nodes and the type of influencing nodes. The importance level of data nodes is determined by quantizing and weighting the location of the data nodes and the parameters of the influencing nodes. Based on the mapping relationship between the part level and the preset part node importance level, the importance level of the part node corresponding to the part level is determined. The preset part node importance level mapping relationship is the correspondence between the part level and the importance level of the part node. The initial data to be filled is determined based on the importance level of the data node and the importance level of the part node, and the data processing strategy for the initial data to be filled is determined based on the mapping relationship between the filling level and the preset processing strategy. Based on the data quality characteristics and preset processing direction mapping relationship corresponding to the initial data to be filled, the data processing direction of the initial data to be filled is determined, and the preset processing direction mapping relationship is the correspondence between data quality characteristics and data processing direction. Based on the data processing strategy and data processing direction corresponding to the initial data to be filled, the initial data to be filled is preprocessed.

3. The method for managing the technical status of rail transit gearbox products according to claim 1, characterized in that, The process for determining the underlying hidden danger data includes: Historical repair data corresponding to a preset observation period is obtained. Historical repair parameters of each part's process equipment are identified from the historical repair data. The historical repair parameters of each part's process equipment are quantified and normalized to obtain the historical repair value of each part's process equipment. The historical repair parameters include historical repair frequency, historical repair degree, and historical repair location. The equipment used for the operation of parts with historical repair values ​​higher than the preset repair threshold is identified as potentially hazardous parts and equipment. The process flow of the potentially hazardous parts and equipment is obtained from the process flow data of rail transit gearbox products. The process priority of the potentially hazardous parts and equipment is determined based on the process flow of the potentially hazardous parts and equipment. Obtain the content to be executed within a preset analysis time period, predict the simulated operation status parameters of the equipment with potential defects during the execution of the content to be executed based on the content to be executed, and identify the simulated defect features contained in the simulated operation status parameters based on the process priority of the equipment with potential defects. The underlying defect data is generated based on the historical repair data and simulated defect features of the defective parts and equipment.

4. The method for managing the technical status of rail transit gearbox products according to claim 3, characterized in that, The process priority for determining the hazardous parts and equipment based on the hazardous process flow includes: The working part parameters corresponding to the equipment with the hidden danger are identified from the process flow of the hidden danger. The working part parameters include the hidden danger part and the relative part position of the hidden danger part in the rail transit gearbox. Obtain historical maintenance records of parts within a preset parts analysis time period, determine the maintenance frequency and maintenance level of the parts with potential problems within the preset parts analysis time period from the historical parts maintenance records, and determine the application importance level of the parts with potential problems based on the maintenance frequency and maintenance level of the parts with potential problems. Based on the relative position of the potentially hazardous parts in the rail transit gearbox, the corresponding part priority weight is determined, and the potential equipment process priority of the potentially hazardous parts is obtained by weighted summation based on the part priority and the application importance level.

5. The method for managing the technical status of rail transit gearbox products according to claim 3, characterized in that, When the number of potential hazards in the defective parts exceeds a preset threshold, the following additional steps are also included: Based on the potential process flow of each potentially hazardous component or equipment, the associated process equipment for each potentially hazardous component or equipment is determined. Obtain the hazard performance parameters of each hazardous component and equipment at each historical analysis stage, and determine the fault migration degree between each hazardous component and equipment and the corresponding associated process equipment at each historical analysis stage based on the hazard performance parameters at each historical analysis stage. Obtain the operational performance parameters of each potentially hazardous component or equipment at each historical analysis stage, and determine the vibration amplitude and temperature change rate of each potentially hazardous component or equipment at each historical analysis stage based on the corresponding operational performance parameters. Based on the fault migration degree, vibration amplitude and temperature change rate of each potentially hazardous component in each historical analysis stage, the health observation value of each potentially hazardous component in each historical analysis stage is determined, and based on the health observation value of each potentially hazardous component in each historical analysis stage, the predicted health status value of each potentially hazardous component in the preset analysis time period is determined. The content to be executed for each hidden component or equipment during the prediction and analysis period is quantified into a predicted workload value. Based on the mapping relationship between the predicted workload value and the preset health value of each hidden component or equipment, the standard health value corresponding to each hidden component or equipment is determined, and the hidden component or equipment whose predicted health value is lower than the corresponding standard health value is identified as a component or equipment to be warned. The health difference between the predicted health status value and the standard health value of the part or equipment to be warned is determined, and the warning level is determined based on the health difference of the part or equipment to be warned and the process priority of the equipment with potential risks. A warning instruction is generated based on the warning level and the part or equipment to be warned.

6. The method for managing the technical status of rail transit gearbox products according to claim 5, characterized in that, Based on the hazard performance parameters from the historical analysis phase, the fault migration degree between the hazard-prone parts / equipment and the corresponding associated process equipment during the historical analysis phase is determined, including: The inherent hazard status parameter value of the potentially hazardous parts and equipment is determined from the hazard performance parameters during the historical analysis phase. The inherent hazard status parameter value is determined by the hazard type and hazard level. Based on the self-hazard status parameter value, the association range is determined. Within the association range, the associated process equipment corresponding to the hazardous part equipment is identified. Based on the hazard performance parameters of the hazardous part equipment and the associated hazard performance parameters of the associated process equipment, the migration rate parameter is determined. The migration rate parameter includes the self-hazard migration rate and the associated hazard migration rate. The migration rate parameter is used to determine the fault migration degree between the defective component / equipment and the corresponding associated process equipment during the historical analysis phase.

7. The method for managing the technical status of rail transit gearbox products according to claim 1, characterized in that, After determining the status management table for visualization technology, the following is also included: Identify feedback nodes from the user task instructions and obtain the acceptable display type corresponding to the feedback nodes; Based on the acceptable display type and the visualization technology status management table, feedback data is determined and fed back to the feedback node.

8. A management system, characterized in that, The management system includes: At least one processor; Memory; At least one application, wherein the at least one application is stored in memory and configured to be executed by at least one processor, the at least one application being configured to: perform a method for managing the technical status of a rail transit gearbox product according to any one of claims 1-7.

9. A computer-readable storage medium, characterized in that, include: The system stores a computer program capable of being loaded by a processor and executed as described in any one of claims 1-7, which is a method for managing the technical status of a rail transit gearbox product.

10. A computer program product, characterized in that, The method includes a computer program that, when executed by a processor, implements the steps of a technical status management method for rail transit gearbox products according to any one of claims 1-7.

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