A digital maintenance platform construction method, device and medium

Abstract

The application discloses a digital maintenance platform construction method and device and a medium, and relates to the technical field of industrial equipment operation and maintenance. The method comprises the following steps: embedding a preset maintenance field model in a general API gateway to construct a business semantic API gateway, taking a maintenance business object as a core input parameter of the business semantic API gateway, and generating a maintenance decision index; integrating an embedded state panel in a standard technical framework of digital maintenance, configuring business operation interaction rules and a maintenance business operation entrance, and obtaining an initial embedded state panel; building a matching link of panel operation and a standard business process of digital maintenance, converting the initial embedded state panel into an adaptive embedded state panel capable of dynamically displaying data; matching the maintenance decision index with a preset maintenance knowledge graph to generate a maintenance decision path corresponding to the maintenance business object, and pushing the maintenance decision path to the adaptive embedded state panel to construct a digital maintenance platform.

Description

Technical Field

[0001] This application relates to the field of industrial equipment operation and maintenance technology, and in particular to a method, equipment and medium for constructing a digital maintenance platform. Background Technology

[0002] Currently, there are two main types of technical solutions in the field of industrial equipment maintenance management, each with its own limitations: Independent digital twin or visual monitoring systems are usually built based on PI Vision, 3D modeling tools, or dedicated large-screen display platforms. They can achieve real-time monitoring and visualization of equipment status, but these systems are mostly deployed independently, resulting in data and process silos with the enterprise's core business systems (such as Enterprise Resource Planning (ERP) systems and equipment maintenance management systems). The alarm information they generate cannot automatically trigger maintenance work orders, and abnormal data cannot be associated with historical maintenance records. Maintenance personnel still need to manually enter maintenance instructions in another system, forming data visualization silos and failing to achieve closed-loop management from status perception to maintenance execution. Secondly, there are traditional maintenance management systems, represented by the equipment maintenance module in ERP systems. Although they can effectively manage maintenance work orders, resource scheduling, and cost accounting, and have complete business process support, their data entry is highly dependent on manual input and lacks the ability to perceive the real-time operating status of equipment. Decision-making is mostly based on post-event reports or static ledgers, resulting in a passive maintenance mode, delayed response, and difficulty in achieving predictive maintenance and intelligent decision-making.

[0003] Therefore, how to achieve deep integration of equipment status data and business processes within the maintenance business system, and realize data-driven intelligent decision-making within the business operation interface, has become an urgent technical problem to be solved. Summary of the Invention

[0004] This application provides a method, device, and medium for constructing a digital maintenance platform to address the following technical problem: how to achieve deep integration of equipment status data and business processes within a maintenance business system, and how to realize data-driven intelligent decision-making within the business operation interface, has become an urgent technical problem to be solved.

[0005] In a first aspect, embodiments of this application provide a method for constructing a digital maintenance platform. The method includes: embedding a preset maintenance domain model in a general API gateway to construct a business semantic API gateway, and using maintenance business objects as the core input parameters of the business semantic API gateway to generate maintenance decision indicators; integrating an embedded status panel in the standard technical framework of digital maintenance, and configuring business operation interaction rules and maintenance business operation entry points for the embedded status panel to obtain an initial embedded status panel; establishing a matching link between panel operations and standard business processes of digital maintenance to transform the initial embedded status panel into an adapted embedded status panel that can dynamically display data; matching maintenance decision indicators with a preset maintenance knowledge graph to generate maintenance decision paths corresponding to maintenance business objects, and pushing the maintenance decision paths to the adapted embedded status panel to construct a digital maintenance platform.

[0006] In one implementation of this application, the method further includes: constructing a maintenance domain model, specifically including: integrating the core business elements of industrial equipment based on the business logic of the entire digital maintenance process to establish a data mapping relationship between the core business elements and maintenance business objects; wherein, the core business elements include equipment status data, historical maintenance work order data, fault diagnosis data, and process parameter data; defining the calculation logic and association rules of maintenance decision indicators based on the core business elements and the data mapping relationship to generate a domain model framework adaptable to maintenance business scenarios; integrating and debugging the domain model framework with the data source access logic of the general API gateway, and verifying the matching of the output maintenance decision indicators with the actual maintenance business needs to complete the construction and optimization of the maintenance domain model.

[0007] In one implementation of this application, a pre-defined maintenance domain model is built into the general API gateway to construct a business semantic API gateway. Specifically, this includes: building the basic access architecture of the general API gateway and encapsulating the access interface to the real-time industrial equipment data source in the basic access architecture to achieve stable data retrieval; and embedding the maintenance domain model into the data processing layer of the general API gateway to generate a business semantic API gateway.

[0008] In one implementation of this application, an embedded status panel is integrated into the standard technical framework of digital maintenance, and business operation interaction rules and maintenance business operation entry points are configured for the embedded status panel to obtain an initial embedded status panel. Specifically, this includes: developing an embedded status panel basic carrier adapted to the standard technical framework based on the native development specifications of the standard technical framework of digital maintenance, and designing the maintenance business operation entry point of the embedded status panel to achieve a precise association between the maintenance business operation entry point and the business operation; formulating business operation interaction rules for the embedded status panel based on the business execution logic of digital maintenance to clarify the business triggering conditions, execution paths, and execution feedback data of the panel operation; and embedding the configured business operation interaction rules and maintenance business operation entry point into the embedded status panel basic carrier to obtain the initial embedded status panel.

[0009] In one implementation of this application, a matching link between panel operation and digital maintenance standard business processes is established to transform the initial embedded status panel into an adapted embedded status panel that can dynamically display data. Specifically, this includes: generating a standardized business process mapping table based on the business triggering conditions and execution feedback data of panel operation; establishing the association between the operation instructions of the initial embedded status panel and each business process node based on the business process mapping table to build a basic matching link between panel operation and standard business processes; configuring a real-time data synchronization mechanism for the basic matching link, and matching the maintenance business objects with the dynamic data display of the initial embedded status panel to generate an adapted embedded status panel that can dynamically display data.

[0010] In one implementation of this application, maintenance decision indicators are matched with a preset maintenance knowledge graph to generate a maintenance decision path corresponding to the maintenance business object. Specifically, this includes: extracting equipment status characteristics and abnormal patterns corresponding to the current maintenance business object based on the maintenance decision indicators; comparing the equipment status characteristics and abnormal patterns with historical fault characteristics stored in the maintenance knowledge graph to obtain historical fault nodes corresponding to the current maintenance business object; retrieving fault causes, solutions, and historical maintenance records associated with the historical fault nodes from the maintenance knowledge graph; and constructing a decision path from the current maintenance business object to the recommended maintenance action based on the retrieved fault causes, solutions, and historical maintenance records.

[0011] In one implementation of this application, the maintenance decision path is pushed to an adapted embedded status panel to build a digital maintenance platform. Specifically, this includes: rendering recommended maintenance actions as several decision suggestion items in the adapted embedded status panel, and binding a corresponding maintenance business operation entry to each suggestion item; when the operator selects any decision suggestion item, activating the bound maintenance business operation entry, and establishing a data transmission channel between the maintenance business operation entry and the digital maintenance standard business process to drive the start of the business process; and transmitting the status feedback and result data during the execution of the business process back to the adapted embedded status panel in real time to achieve a visual presentation of the decision execution closed loop.

[0012] In one implementation of this application, after constructing the digital maintenance platform, the method further includes: extracting business execution data from the maintenance decision path; wherein the business execution data includes real-time process parameters, predicted values, and equipment degradation caused by the predicted values; and associating and matching the business execution data with the abnormal data patterns recorded by the adapted embedded status panel during the maintenance execution process to generate alarms and generate maintenance suggestions.

[0013] Secondly, embodiments of this application also provide a digital maintenance platform construction device, the device comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to: embed a preset maintenance domain model in a general API gateway to construct a business semantic API gateway, and use maintenance business objects as core input parameters of the business semantic API gateway to generate maintenance decision indicators; integrate an embedded status panel in the standard technical framework of digital maintenance, and configure business operation interaction rules and maintenance business operation entry points for the embedded status panel to obtain an initial embedded status panel; establish a matching link between panel operations and standard digital maintenance business processes to transform the initial embedded status panel into an adapted embedded status panel that can dynamically display data; match maintenance decision indicators with a preset maintenance knowledge graph to generate maintenance decision paths corresponding to maintenance business objects, and push the maintenance decision paths to the adapted embedded status panel to construct a digital maintenance platform.

[0014] Thirdly, embodiments of this application also provide a non-volatile computer storage medium for constructing a digital maintenance platform, storing computer-executable instructions. These computer-executable instructions are configured to: embed a pre-defined maintenance domain model within a general API gateway to construct a business semantic API gateway, using maintenance business objects as core input parameters to generate maintenance decision indicators; integrate an embedded status panel within the standard technical framework of digital maintenance, configuring business operation interaction rules and maintenance business operation entry points for the embedded status panel to obtain an initial embedded status panel; establish a matching link between panel operations and standard digital maintenance business processes to transform the initial embedded status panel into an adapted embedded status panel capable of dynamically displaying data; match maintenance decision indicators with a pre-defined maintenance knowledge graph to generate maintenance decision paths corresponding to maintenance business objects, and push the maintenance decision paths to the adapted embedded status panel to construct a digital maintenance platform.

[0015] The digital maintenance platform construction method, equipment, and medium provided in this application have the following beneficial effects: A maintenance domain model is built into a general API gateway, and maintenance business objects are used as core input parameters. This allows the API gateway to no longer output meaningless raw data streams, but instead directly generate maintenance decision indicators, realizing the semantic transformation of real-time process data into business data and deeply binding real-time equipment data with maintenance business objects. Furthermore, the embedded status panel is integrated into the digital maintenance standard technology framework, enabling the display and analysis of real-time equipment data to be directly implemented in the maintenance business operation interface, allowing maintenance personnel to obtain equipment status data in business operation scenarios without needing to switch systems. Attached Figure Description

[0016] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0017] Figure 1 A flowchart illustrating a method for constructing a digital maintenance platform, as provided in this application embodiment;

[0018] Figure 2 This is a schematic diagram of the internal structure of a digital maintenance platform construction device provided in an embodiment of this application. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0020] This application provides a method, device, and medium for constructing a digital maintenance platform to address the following technical problem: how to achieve deep integration of equipment status data and business processes within a maintenance business system, and how to realize data-driven intelligent decision-making within the business operation interface, has become an urgent technical problem to be solved.

[0021] The technical solutions proposed in the embodiments of this application will be described in detail below with reference to the accompanying drawings.

[0022] Figure 1 This is a flowchart illustrating a method for constructing a digital maintenance platform, as provided in an embodiment of this application. Figure 1 As shown in the figure, the method for constructing a digital maintenance platform provided in this application embodiment specifically includes the following steps:

[0023] Step 10: Integrate a pre-defined maintenance domain model into the general API gateway to build a business semantic API gateway, and use maintenance business objects as the core input parameters of the business semantic API gateway to generate maintenance decision indicators.

[0024] As an optional embodiment, a preset maintenance domain model is built into the general API gateway to build a business semantic API gateway, which may specifically include: Step 101: Building a maintenance domain model.

[0025] As an optional embodiment, constructing a maintenance domain model may specifically include: Step 1011: Based on the business logic in the entire digital maintenance process, integrate the core business elements of industrial equipment to establish a data mapping relationship between the core business elements and maintenance business objects.

[0026] This step takes the entire digital maintenance process as its framework. First, following the full business logic from equipment status perception, anomaly identification, work order execution to fault resolution, it decomposes and integrates core business elements related to maintenance, such as equipment status, fault diagnosis, and operation and maintenance management. This ensures that the integrated elements accurately match the needs of each maintenance stage, becoming the core data carrier connecting equipment operating status and maintenance operations. Based on this, using maintenance business objects as the core association, core equipment business elements are bound to equipment business objects such as equipment number and functional location. Fault warning elements are associated with process business objects such as maintenance work orders and notifications, achieving a deep two-way binding between core business elements and maintenance business objects. Simultaneously, the mapping relationship follows maintenance business semantic specifications, relying on maintenance domain model optimization rules to ensure the rationality of the association and possess dynamic adaptability. It can be flexibly adjusted according to changes in business logic, core elements, and business objects, laying a solid data foundation for subsequent business semantic API gateway construction and dynamic embedded status panel display, achieving native integration of equipment data and maintenance business.

[0027] Step 1012: Based on the core business elements and data mapping relationship, define the calculation logic and association rules of maintenance decision indicators to generate a domain model framework that can be adapted to maintenance business scenarios.

[0028] In this step, relying on the established data mapping relationship between core business elements and maintenance business objects, each maintenance decision indicator is precisely linked to its corresponding core business element. Following the actual business logic of industrial equipment operation and maintenance management, specific calculation methods for the indicators are designed, allowing them to be calculated from real-time data of core business elements, truly reflecting the actual state of the maintenance business object. Based on the data mapping relationship, the matching relationship between maintenance decision indicators and different maintenance business objects and different maintenance operation scenarios is clarified. Simultaneously, the business relationships between each indicator are analyzed, enabling the indicator system to achieve precise linkage according to the type of maintenance business object and the actual needs of business operations. On this basis, the implemented calculation logic and association rules are systematically integrated and encapsulated to form a standardized maintenance domain model framework. This framework directly connects to maintenance business scenarios, enabling rapid processing of real-time equipment data and output of pre-calculated business indicators required for maintenance decisions based on core business elements and data mapping relationships.

[0029] Step 1013: Integrate and debug the domain model framework with the data source access logic of the general API gateway, and verify the matching of the output maintenance decision indicators with the actual maintenance business needs, so as to complete the construction and optimization of the maintenance domain model.

[0030] In this step, after completing the construction of the maintenance domain model framework, the pre-defined maintenance decision indicator calculation logic and association rules in the domain model framework are integrated and adapted with the data source access logic of the general API gateway. Simultaneously, data processing and indicator calculation are completed according to the rules set in the model, ensuring seamless integration between the gateway's data source access, data processing flow, and the business logic of the domain model. During debugging, the focus is on verifying the smoothness of the integrated link, identifying connection issues in data retrieval, transmission, and calculation, and ensuring that the general API gateway stably outputs standardized maintenance decision indicators, rather than meaningless raw measurement point data. After completing the integration and debugging, the gateway is further optimized based on the actual business scenarios and decision-making needs of the entire digital maintenance process. The output maintenance decision indicators undergo multi-dimensional verification to confirm whether they accurately reflect the actual state of maintenance business objects and precisely match the decision-making requirements of different maintenance operation scenarios. Simultaneously, the output efficiency and data accuracy of the indicators are verified to ensure they meet business execution requirements. For issues discovered during verification, such as mismatches between indicators and business needs, unreasonable calculation logic, and data retrieval deviations, the calculation logic and association rules of the domain model framework are adjusted and optimized in reverse. Simultaneously, the data source access logic of the general API gateway is adapted. Through multiple rounds of integration, debugging, verification, and optimization, the business logic of the maintenance domain model is highly compatible with the technical implementation of the API gateway, ultimately completing the construction of a maintenance domain model that combines business adaptability and technical stability.

[0031] Step 102: Build the basic access architecture of the general API gateway, and encapsulate the access interface to the real-time industrial equipment data source in the basic access architecture to achieve stable data retrieval.

[0032] In this step, based on the access characteristics of industrial equipment data sources and the real-time data access requirements of maintenance operations, core layers such as the data source access layer, data transmission layer, and interface call layer are defined. The technical responsibilities and interaction logic of each layer are clarified, enabling the architecture to adapt to different types of real-time industrial equipment data sources. After completing the basic access architecture, standardized adaptation interfaces are developed based on the communication protocols and data formats of various industrial equipment real-time data sources to achieve unified access and standardized access to different data sources, avoiding data retrieval compatibility issues caused by differences in data source types. Data verification, exception handling, and fault tolerance mechanisms are set up simultaneously to respond and handle issues such as interruptions, delays, or format deviations during data retrieval in a timely manner, ensuring the stability of data retrieval. The encapsulated access interfaces are uniformly managed and scheduled, allowing the general API gateway to accurately retrieve the raw data required by maintenance operations from real-time industrial equipment data sources on demand through standardized interfaces, achieving stable data transmission and retrieval from the device end to the gateway end.

[0033] Step 103: Integrate the maintenance domain model into the data processing layer of the general API gateway to generate a business semantic API gateway.

[0034] In this step, after constructing the maintenance domain model, it is deeply integrated with the existing data processing logic of the data processing layer. This allows the gateway to directly process raw data retrieved from real-time data sources using the built-in maintenance domain model, according to preset calculation logic and association rules, instead of simply outputting raw measurement point data streams without business meaning. Simultaneously, maintenance business objects are set as the core input parameters of the business semantic API gateway interface. This allows the gateway to accurately match corresponding core business elements and data mapping relationships based on the accessed maintenance business object information, completing targeted indicator calculations and data integration, and directly outputting pre-calculated standardized business indicators required for maintenance decisions. Furthermore, this business semantic API gateway features a lightweight encapsulation design tailored to maintenance business scenarios. The embedded maintenance domain model seamlessly integrates with the gateway's data source access logic and data transmission logic, ensuring efficient and stable data processing. Ultimately, this upgrades the general API gateway into a dedicated gateway with maintenance business semantic parsing capabilities, enabling the direct conversion from real-time equipment data to maintenance business decision information.

[0035] Step 20: Integrate the embedded status panel into the standard technical framework of digital maintenance, and configure the business operation interaction rules and maintenance business operation entry points for the embedded status panel to obtain the initial embedded status panel.

[0036] As an optional embodiment, an embedded status panel is integrated into the standard technical framework of digital maintenance, and business operation interaction rules and maintenance business operation entry points are configured for the embedded status panel to obtain an initial embedded status panel. Specifically, it may include: Step 201: Based on the native development specifications of the standard technical framework of digital maintenance, develop an embedded status panel basic carrier adapted to the standard technical framework, and design the maintenance business operation entry point of the embedded status panel to achieve accurate association between the maintenance business operation entry point and the business operation.

[0037] In this step, the first step is to build an embedded status panel base that is compatible with the framework. During development, the framework's native development specifications and technical requirements are strictly followed. The base is built using the framework's built-in development components and technical architecture, without relying on third-party portals or external development frameworks. This ensures the base becomes a native component of the standard digital maintenance interface, guaranteeing stable panel operation within the framework and smooth access to the system's standard functions and data resources. After completing the base construction, the core business operations in the entire digital maintenance process are identified, clarifying the business logic and execution chain of each operation. Then, based on the panel's display format and interaction logic, corresponding standardized operation entry points are set up within the panel. The entry point layout is optimized according to the sequence of business processes and operation frequency. Simultaneously, a direct link is established between the entry points and backend business operations, allowing users to directly invoke the corresponding business process when triggering an operation entry point in the panel, without needing to navigate to other system interfaces. This upgrades the embedded status panel from a simple data display carrier into an interactive terminal capable of directly conducting business operations, aligning with the actual operating habits of maintenance personnel.

[0038] Step 202: Based on the business execution logic of digital maintenance, formulate business operation interaction rules for the embedded status panel to clarify the business triggering conditions, execution paths and execution feedback data of the panel operation.

[0039] This step first involves a deep breakdown of the complete business execution logic of digital maintenance, from anomaly identification and work order initiation to process execution and fault resolution. It involves identifying the business triggering nodes, operational execution standards, and data feedback requirements for each stage, and using this as the basis to build the overall framework for panel interaction rules, ensuring that the rules align with the operational habits and execution requirements of actual maintenance work. Secondly, it clarifies the corresponding business triggering conditions, determining the business scenarios, equipment states, or operational contexts under which the panel operation can be triggered, ensuring that the initiation of panel operations matches the actual needs of the maintenance business and avoiding invalid operation triggers. Simultaneously, it plans the specific details of each panel operation in conjunction with the standard business processes of digital maintenance. The execution path clearly defines the functions to be invoked, the business modules to be connected, and the sequence of execution steps after an operation is triggered. This allows panel operations to directly connect to the standard processes of the maintenance system without the need for manual intervention. In addition, it determines the execution feedback data corresponding to each operation, specifying the business processing results, process node status, and related business data that the panel should display after the operation is executed. This allows operators to obtain real-time feedback on the operation execution directly from the panel without having to jump to other interfaces for querying. This not only ensures the rationality and effectiveness of the embedded status panel operation, but also makes the panel a native component of the maintenance business process, further strengthening the integration between the panel and the maintenance business system and improving the execution efficiency of maintenance operations.

[0040] Step 203: Implant the configured business operation interaction rules and maintenance business operation entry into the embedded status panel base carrier to obtain the initial embedded status panel.

[0041] In this step, after completing the development of the embedded status panel's basic carrier, the design of the maintenance business operation entry point, and the formulation of business operation interaction rules, the configured interaction rules and operation entry points are uniformly embedded into the basic carrier. The embedding process strictly follows the native development specifications of the digital maintenance standard technical framework to ensure seamless integration of the operation entry point, interaction rules, and basic carrier at the technical level, allowing various preset configuration contents to take effect stably based on the carrier's native operating environment. For the maintenance business operation entry point, according to the previous layout design and association logic, it is precisely deployed in the corresponding display area of ​​the panel's basic carrier to ensure that the presentation form of the entry point matches the overall interaction logic of the panel, while retaining the direct association link between the entry point and the backend maintenance business operations, ensuring that the corresponding business functions can be smoothly called after the entry point is triggered. For the business operation interaction rules, they are embedded as the core logic layer into the backend processing module of the panel's basic carrier, so that the rules can constrain and guide all operation behaviors on the panel, clarify the triggering conditions, execution paths, and feedback requirements of each operation entry point, and ensure that each business operation on the panel can be carried out in an orderly manner according to the preset rules.

[0042] Step 30: Establish a matching link between panel operation and digital maintenance standard business processes to transform the initial embedded status panel into an adapted embedded status panel that can dynamically display data.

[0043] As an optional embodiment, a matching link between panel operation and digital maintenance standard business process is established to transform the initial embedded status panel into an adapted embedded status panel that can dynamically display data. Specifically, it may include: Step 301: Based on the business triggering conditions and execution feedback data of panel operation, a standardized business process mapping table is generated.

[0044] This step begins by comprehensively reviewing the preset business triggering conditions for all maintenance operations in the embedded status panel. It clarifies the business scenarios, equipment status, and operational contexts that can trigger various operations. Simultaneously, it organizes the data types, dimensions, and display formats of the execution feedback for each operation, ensuring complete collection of all dimensions of information about the panel operations. Based on this, the triggering conditions, execution paths, and corresponding execution feedback data for each panel operation are precisely matched with specific nodes, execution specifications, and result feedback requirements in the maintenance business process, establishing a correspondence between panel operations and business process nodes. Subsequently, a business process mapping table is built according to unified specifications and formats. This table clearly defines the association between each panel operation and the standard maintenance business process, specifying the specific business process that the panel operation will connect to under different triggering conditions, the specific data content to be fed back after the operation, and marking the key nodes and connection requirements for operation execution. This ensures that each operation on the panel accurately connects to the standard business process in the backend, and that the execution results of the business process are fed back to the panel according to preset requirements, guaranteeing that the linkage between panel operations and the maintenance business process always aligns with the business execution logic of digital maintenance.

[0045] Step 302: Based on the business process mapping table, establish the association between the operation instructions of the initial embedded status panel and each business process node, so as to build a basic matching link between panel operation and standard business process.

[0046] In this step, using the business process mapping table as a standardized reference, key information such as the digital maintenance standard business process nodes and execution requirements corresponding to each panel operation command is extracted from the table. This clarifies the specific business process link that each panel operation command needs to connect to. Then, according to the association rules of the mapping table, a dedicated association link is configured for each operation command of the initial embedded status panel, deeply binding the operation command with the corresponding business process node. This makes the panel operation command a direct entry point for triggering standard business process nodes, ensuring that after the operation command is triggered, it can accurately connect to the corresponding process node and promote the orderly execution of the process according to the standard business logic of digital maintenance. When establishing the association relationship, the business execution logic of digital maintenance is strictly followed to ensure that the association order and triggering mechanism of the operation command and business process node are consistent with the actual maintenance business execution process, avoiding the problem of misalignment between operation and process. Finally, a basic matching link between panel operation and standard business process is built, allowing the operation of the initial embedded status panel to directly connect to the standard business process of digital maintenance, laying the foundation for process linkage for the subsequent panel to realize dynamic data display and intelligent business interaction.

[0047] Step 303: Configure a real-time data synchronization mechanism for the basic matching link, and match the maintenance business objects with the dynamic data display of the initial embedded status panel to generate an adapted embedded status panel that can dynamically display data.

[0048] In this step, the first step is to build a full-link real-time data interaction channel around the digital maintenance business logic, connecting the panel with the business semantic API gateway and the standard maintenance business system. Synchronization trigger nodes and update frequencies are set to ensure that business process status data triggered by panel operations and real-time equipment status data can be transmitted back in real time, and panel operation data can also be synchronized to the business system for archiving, ensuring data consistency and timeliness. Secondly, based on the existing data mapping relationship between core business elements and maintenance business objects, the panel's dynamic display module is deeply bound to the corresponding business object. This allows the panel to automatically retrieve the corresponding pre-calculated maintenance decision indicators and real-time equipment data from the API gateway according to the currently processed business object, achieving precise adaptation of data display to business objects. After the above operations, the initial panel possesses real-time data perception and dynamic display capabilities, becoming an adapted panel deeply bound to maintenance business objects and linked in real time with standard business processes, achieving native integration of equipment data, business objects, business processes, and panel display.

[0049] Step 40: Match maintenance decision indicators with the preset maintenance knowledge graph to generate maintenance decision paths corresponding to maintenance business objects, and push the maintenance decision paths to the adapted embedded status panel to build a digital maintenance platform.

[0050] As an optional embodiment, maintenance decision indicators are matched with a preset maintenance knowledge graph to generate a maintenance decision path corresponding to the maintenance business object, and the maintenance decision path is pushed to the adapted embedded status panel to build a digital maintenance platform. Specifically, it may include: Step 401: Based on the maintenance decision indicators, extract the equipment status characteristics and abnormal patterns corresponding to the current maintenance business object.

[0051] In this step, the maintenance decision indicators output by the business semantic API gateway serve as the core data basis. Combining the professional logic of industrial equipment operation and maintenance with the business needs of digital maintenance, targeted features and patterns are extracted from the real-time operation data of the equipment deeply bound to the current maintenance business object. Focusing on the equipment operating status dimension reflected by the maintenance decision indicators, core status features that reflect the actual working conditions of the equipment are selected, discarding irrelevant and redundant data dimensions to ensure that the extracted status features align with the actual needs of maintenance decisions and truly reflect the operating status of the equipment. Simultaneously, based on key information such as equipment status deviation thresholds and trend changes reflected in the maintenance decision indicators, and combined with the normal operating status benchmark of the equipment, the abnormal patterns of the equipment corresponding to the current maintenance business object are identified, clarifying the manifestations of deviations from normal operating patterns. The entire extraction process remains closely linked to the current maintenance business object, ensuring that the extracted equipment status features and abnormal patterns are specific to this business object, rather than generalized equipment data. This provides accurate and effective status basis for subsequent matching with the maintenance knowledge graph and the construction of maintenance decision paths.

[0052] Step 402: Compare the equipment status characteristics and abnormal patterns with the historical fault characteristics stored in the maintenance knowledge graph to obtain the historical fault nodes corresponding to the current maintenance business object.

[0053] In this step, the precise comparison of equipment status characteristics and anomaly patterns with historical fault characteristics stored in the maintenance knowledge graph is a crucial link between real-time equipment status and historical maintenance experience, and for locating the fault type corresponding to the current equipment anomaly. This process relies on the structured storage logic and feature matching rules of the maintenance knowledge graph to achieve a precise association between the current equipment anomaly and historical fault cases. During the comparison, the extracted equipment status characteristics and anomaly patterns specific to the current maintenance business object are used as the core retrieval basis. According to the professional feature dimensions of industrial equipment operation and maintenance, a multi-dimensional and refined matching analysis is performed with the historical fault characteristics archived in the maintenance knowledge graph, breaking the limitations of single feature matching and taking into account... The similarity of features and the fit of abnormal patterns are considered. At the same time, relying on the hierarchical and associated storage design of historical faults based on the maintenance knowledge graph, various related attributes of faults are associated in the feature comparison to ensure the accuracy and fit of the comparison results. From the massive historical fault data, historical fault content that highly matches the current equipment status characteristics and abnormal patterns is screened out. Finally, the historical fault node corresponding to the current maintenance business object is locked. This historical fault node serves as a key hub connecting the current equipment abnormality and historical maintenance data, providing a precise retrieval entry point for subsequent retrieval of the corresponding fault causes, solutions and maintenance records, so that historical maintenance experience can effectively empower the current intelligent maintenance decision-making.

[0054] Step 403: Retrieve the causes of failures, solutions, and historical maintenance records associated with historical failure nodes from the maintenance knowledge graph.

[0055] In this step, the matched historical fault nodes serve as the core retrieval anchor points. Comprehensive, multi-dimensional maintenance-related data deeply associated with these nodes is retrieved from the knowledge graph. This includes the core causes of the faults, proven effective solutions from past practices, and corresponding complete historical maintenance records. The retrieval process strictly adheres to the hierarchical relationships between fault nodes and various types of maintenance data within the knowledge graph. This ensures that the retrieved fault causes accurately correspond to the triggering mechanisms of the fault type, that the solutions align with the actual handling needs of the fault, and that the historical maintenance records fully recreate the past handling processes, operational details, and handling results of the fault. Simultaneously, based on the business requirements of digital maintenance, the retrieved content is categorized and organized, eliminating redundant information irrelevant to the current maintenance business, ensuring the relevance and effectiveness of the retrieved data, and allowing various types of maintenance data to form a mutually supportive and complete system.

[0056] Step 404: Based on the retrieved fault causes, solutions, and historical maintenance records, construct a decision path from the current maintenance business object to the recommended maintenance action.

[0057] This step begins by analyzing the adaptability of historical fault causes based on the equipment status characteristics, anomaly patterns, and actual maintenance scenarios of the current maintenance business object. This clarifies the core triggering factors and impact dimensions of such faults in the current scenario, eliminating historical fault causes irrelevant to the current business object. Next, past solutions are screened and optimized, selecting the most adaptable solution from historically effective solutions based on current maintenance resource allocation and on-site operating conditions. Implementation details are then adjusted and optimized according to the current equipment status to ensure the solution meets actual execution needs. Simultaneously, referencing the operational processes, key nodes, and precautions compiled from historical maintenance records, the standard business processes of digital maintenance are integrated, clarifying the execution requirements for each stage from fault confirmation to maintenance action implementation. Based on this, a complete logical chain is outlined from anomaly identification and fault location of the current maintenance business object to the selection and implementation of specific maintenance actions, clarifying the execution order, operational basis, and connection requirements of each stage, thus constructing a clear and executable decision-making path.

[0058] Step 405: Render the recommended maintenance action as several decision suggestion items in the adapted embedded status panel, and bind the corresponding maintenance business operation entry to each suggestion item.

[0059] In this step, the compiled recommended maintenance actions are transformed into several decision suggestions that can be displayed on an adapted embedded status panel. Based on the business attributes and execution priorities of the maintenance actions, standardized rendering is used to present these suggestions, allowing maintenance personnel to intuitively and clearly view various targeted maintenance decision suggestions on the panel. The display format of each suggestion item is consistent with the overall interaction logic and visual design of the panel, conforming to the operational viewing habits of maintenance personnel. After rendering the decision suggestion items, each suggestion item is precisely bound to a corresponding maintenance business operation entry point. This process is based on the standard business process of digital maintenance, establishing a unique and direct association between the specific business operation step corresponding to each recommended maintenance action and the operation entry point within the panel. This ensures that each decision suggestion item can be connected to the core business operation entry point for implementing the maintenance action. After binding, when maintenance personnel select any decision suggestion item in the panel, they can directly trigger the corresponding maintenance business operation entry point without having to search for an operation path, achieving a seamless connection from maintenance decision suggestions to actual business operations.

[0060] Step 406: When the operator selects any decision suggestion item, the bound maintenance business operation entry is activated, and a data transmission channel is established between the maintenance business operation entry and the digital maintenance standard business process to drive the start of the business process.

[0061] In this step, when the operator selects any decision suggestion item in the adapted embedded status panel, the pre-bound maintenance operation entry for that suggestion item will be activated immediately. This ensures that the linkage response between the operation entry and the decision suggestion is accurate and timely, allowing the maintenance personnel's decision to be directly converted into a business operation start signal. Simultaneously, a dedicated data transmission channel is established between the maintenance operation entry and the digital maintenance standard business process. This channel is built on the standard technical framework of digital maintenance and strictly follows the native data interaction specifications of the business system, enabling efficient and stable data transmission between the operation entry and the corresponding business process. Through this data transmission channel, core data such as equipment status data, fault matching information, and maintenance decision-related parameters related to the current maintenance business object and decision suggestion item will be accurately and completely transmitted to the corresponding start node of the digital maintenance standard business process, providing comprehensive and demand-compliant basic data support for the start of the business process.

[0062] Step 407: Real-time transmission of status feedback and result data during the execution of the business process to the adapted embedded status panel to achieve a visual presentation of the decision execution closed loop.

[0063] In this step, the status feedback data covers core information such as the execution progress, operation completion status, and process connection status of each node in the business process. The result data includes key content such as the fault handling effect after process execution, equipment status changes, and maintenance operation implementation results. Both types of data rely on the previously established real-time data synchronization mechanism to accurately and timely transmit data from the business process end to the panel, ensuring the timeliness and completeness of data feedback. The adapted embedded status panel presents the returned status feedback and result data in a structured and visual manner according to the preset display logic. This allows operators to intuitively view the entire process of maintenance decision suggestions being implemented into business processes on the same panel without having to jump to other systems or interfaces to query. From the selection of decision suggestion items and the start of the business process to the execution status of each link in the process and the final maintenance execution result, all data and status of the entire decision execution closed loop can be clearly presented on the panel. At the same time, the panel will also associate the status feedback and result data of this execution with the previous equipment status characteristics and fault matching information to form a complete maintenance decision execution data link. This not only allows operators to keep abreast of the dynamics of business process execution in real time, but also provides complete practical data support for subsequent maintenance knowledge accumulation and fault diagnosis optimization.

[0064] Step 408: Extract the business execution data from the maintenance decision path; whereby the business execution data includes real-time process parameters, predicted values, and equipment degradation caused by the predicted values.

[0065] In this step, during the implementation of the maintenance decision-making path, various types of business execution data are comprehensively extracted from the decision-making path according to the business execution logic of digital maintenance. The extracted business execution data covers multiple key dimensions, including real-time process-related parameters continuously generated and synchronously collected during equipment operation, as well as state prediction-related numerical content based on model inference, and further includes related data on the overall equipment deterioration changes caused by the prediction results. All types of data are systematically collected and structured according to the advancement nodes of the decision-making path, maintaining the temporal and business correlation between different types of data. This ensures that the extracted business execution data can completely restore the entire decision-making execution process, the real-time operating status of the equipment, and the evolution of equipment deterioration caused by the predicted trends, providing comprehensive and complete underlying data support for subsequent data association matching, knowledge accumulation, and model iteration optimization.

[0066] Step 409: Associate and match the business execution data with the abnormal data patterns recorded in the adapted embedded status panel during the maintenance process to generate alarms and maintenance suggestions.

[0067] In this step, during the implementation of the maintenance decision-making path, the current maintenance business object is used as the core link. Real-time process parameters, predicted values, and equipment degradation-related data in the business execution data are compared and analyzed from multiple dimensions with the abnormal data patterns recorded on the panel. This accurately identifies the correlation and points of convergence between the two, determining the specific business execution scenario, parameter change trend, and equipment degradation state corresponding to the abnormal equipment patterns during business execution. This ensures that the matching results accurately reflect the correspondence between equipment abnormalities and maintenance business execution. When an abnormal convergence is identified between the business execution data and the abnormal data pattern during the correlation matching process, and this... When the preset alarm trigger conditions are met, the alarm mechanism is triggered immediately, generating targeted equipment anomaly alarm information. The alarm information will be pushed to the adapted embedded status panel in an intuitive way to remind operators to pay attention to the current status of the equipment and intervene in a timely manner. At the same time, based on the result of the correlation and matching between the two, combined with the historical fault data, fault causes and solutions stored in the maintenance knowledge graph, the system will automatically deduce and generate maintenance suggestions adapted to the current abnormal equipment status and business execution scenario, clarifying the core handling direction and key operation points for this abnormal mode, ensuring that the maintenance suggestions are highly targeted and implementable.

[0068] The above are embodiments of the method proposed in this application. Based on the same inventive concept, embodiments of this application also provide a digital maintenance platform construction device, the structure of which is as follows: Figure 2 As shown.

[0069] Figure 2 This is a schematic diagram of the internal structure of a digital maintenance platform construction device provided in an embodiment of this application. Figure 2 As shown, the device includes:

[0070] At least one processor 201;

[0071] And a memory 202 that is communicatively connected to at least one processor;

[0072] The memory 202 stores instructions executable by at least one processor. These instructions are executed by at least one processor 201 to enable the processor 201 to: embed a pre-defined maintenance domain model into a general API gateway to construct a business semantic API gateway, using maintenance business objects as core input parameters to generate maintenance decision indicators; integrate an embedded status panel into the standard technical framework of digital maintenance, configuring business operation interaction rules and maintenance business operation entry points for the embedded status panel to obtain an initial embedded status panel; establish a matching link between panel operations and standard digital maintenance business processes to transform the initial embedded status panel into an adapted embedded status panel that can dynamically display data; and match maintenance decision indicators with a pre-defined maintenance knowledge graph to generate maintenance decision paths corresponding to maintenance business objects, pushing the maintenance decision paths to the adapted embedded status panel to construct a digital maintenance platform.

[0073] Some embodiments of this application provide corresponding to Figure 1 A non-volatile computer storage medium for constructing a digital maintenance platform stores computer-executable instructions. These instructions are configured to: embed a pre-defined maintenance domain model within a general API gateway to construct a business semantic API gateway, using maintenance business objects as core input parameters to generate maintenance decision indicators; integrate an embedded status panel within the standard technical framework of digital maintenance, configuring business operation interaction rules and maintenance business operation entry points for the embedded status panel to obtain an initial embedded status panel; establish a matching link between panel operations and standard digital maintenance business processes to transform the initial embedded status panel into an adapted embedded status panel capable of dynamically displaying data; match maintenance decision indicators with a pre-defined maintenance knowledge graph to generate maintenance decision paths corresponding to maintenance business objects, and push these paths to the adapted embedded status panel to construct the digital maintenance platform.

[0074] The various embodiments in this application are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the embodiments for IoT devices and media are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0075] The systems, media, and methods provided in this application are one-to-one correspondences. Therefore, the systems and media also have similar beneficial technical effects as their corresponding methods. Since the beneficial technical effects of the methods have been described in detail above, the beneficial technical effects of the systems and media will not be repeated here.

[0076] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0077] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0078] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0079] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0080] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0081] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0082] Computer-readable media include both permanent and non-permanent, removable and non-removable media that can store information by any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0083] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0084] The above description is merely an embodiment of this application and is not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A method for constructing a digital maintenance platform, characterized in that, The method includes: A pre-defined maintenance domain model is built into the general API gateway to construct a business semantic API gateway. The maintenance business object is used as the core input parameter of the business semantic API gateway to generate maintenance decision indicators. The maintenance business object includes equipment location, equipment number, and work order number. The maintenance decision indicators include health score, predictive maintenance priority, and frequency of similar failures. An embedded status panel is integrated into the standard technical framework of digital maintenance, and business operation interaction rules and maintenance business operation entry points are configured for the embedded status panel to obtain an initial embedded status panel. Establish a matching link between panel operation and digital maintenance standard business processes to transform the initial embedded status panel into an adapted embedded status panel that can dynamically display data. The maintenance decision indicators are matched with a preset maintenance knowledge graph to generate a maintenance decision path corresponding to the maintenance business object, and the maintenance decision path is pushed to the adapted embedded status panel to build a digital maintenance platform. Constructing a maintenance domain model specifically includes: Based on the business logic of the entire digital maintenance process, the core business elements of industrial equipment are integrated to establish a data mapping relationship between the core business elements and maintenance business objects; wherein, the core business elements include equipment status data, historical maintenance work order data, fault diagnosis data and process parameter data; Based on the core business elements and the data mapping relationship, the calculation logic and association rules of the maintenance decision indicators are defined to generate a domain model framework that can be adapted to maintenance business scenarios. The domain model framework is integrated and debugged with the data source access logic of the general API gateway, and the matching of the output maintenance decision indicators with the actual maintenance business needs is verified, so as to complete the construction and optimization of the maintenance domain model. A pre-defined maintenance domain model is built into the general API gateway to construct a business semantic API gateway, specifically including: The basic access architecture of the general API gateway is built, and the access interface to the real-time industrial equipment data source is encapsulated in the basic access architecture to achieve stable data retrieval. The maintenance domain model is embedded into the data processing layer of the general API gateway to generate the business semantic API gateway; An embedded status panel is integrated into the standard technical framework of digital maintenance, and business operation interaction rules and maintenance business operation entry points are configured for the embedded status panel to obtain an initial embedded status panel, specifically including: Based on the native development specifications of the standard technical framework for digital maintenance, an embedded status panel basic carrier adapted to the standard technical framework is developed, and the maintenance business operation entry of the embedded status panel is designed to achieve a precise association between the maintenance business operation entry and the business operation. Based on the business execution logic of digital maintenance, business operation interaction rules for the embedded status panel are formulated to clarify the business triggering conditions, execution paths and execution feedback data of the panel operation; The configured business operation interaction rules and the maintenance business operation entry are embedded into the embedded status panel base carrier to obtain the initial embedded status panel; Establish a matching link between panel operation and digital maintenance standard business processes to transform the initial embedded status panel into an adapted embedded status panel that can dynamically display data. Specifically, this includes: Based on the business triggering conditions and execution feedback data of the panel operation, a standardized business process mapping table is generated; Based on the business process mapping table, establish the association between the operation instructions of the initial embedded status panel and each business process node, so as to build a basic matching link between panel operation and standard business process; Configure a real-time data synchronization mechanism for the basic matching link, and match the maintenance business object with the dynamic data display of the initial embedded status panel to generate an adapted embedded status panel that can dynamically display data.

2. The method for constructing a digital maintenance platform according to claim 1, characterized in that, The maintenance decision indicators are matched with a preset maintenance knowledge graph to generate a maintenance decision path corresponding to the maintenance business object, specifically including: Based on the maintenance decision indicators, extract the equipment status characteristics and abnormal patterns corresponding to the current maintenance business object; The device status characteristics and the abnormal patterns are compared with the historical fault characteristics stored in the maintenance knowledge graph to obtain the historical fault nodes corresponding to the current maintenance business object. Retrieve the causes of failures, solutions, and historical maintenance records associated with the historical failure nodes from the maintenance knowledge graph; Based on the retrieved fault causes, solutions, and historical maintenance records, a decision path is constructed from the current maintenance business object to the recommended maintenance action.

3. The method for constructing a digital maintenance platform according to claim 2, characterized in that, The maintenance decision path is pushed to the adapted embedded status panel to build a digital maintenance platform, specifically including: The recommended maintenance action is rendered as several decision suggestion items in the adapted embedded status panel, and each suggestion item is bound to the corresponding maintenance business operation entry. When an operator selects any decision suggestion item, the bound maintenance business operation entry is activated, and a data transmission channel is established between the maintenance business operation entry and the digital maintenance standard business process to drive the start of the business process. The status feedback and result data during the execution of the business process are transmitted back to the adapted embedded status panel in real time to realize the visual presentation of the decision execution closed loop.

4. The method for constructing a digital maintenance platform according to claim 1, characterized in that, After constructing the digital maintenance platform, the method further includes: Extract the business execution data from the maintenance decision path; wherein, the business execution data includes real-time process parameters, predicted values, and equipment degradation caused by the predicted values; The business execution data is correlated and matched with the abnormal data patterns recorded by the adapted embedded status panel during the maintenance process to generate alarms and maintenance suggestions.

5. A digital maintenance platform construction device, characterized in that, The device includes: At least one processor; And, a memory communicatively connected to the at least one processor; The memory stores instructions that can be executed by the at least one processor, which are executed by the at least one processor to enable the at least one processor to perform a digital maintenance platform construction method as described in any one of claims 1-4.

6. A non-volatile computer storage medium for constructing a digital maintenance platform, storing computer-executable instructions, characterized in that, When the computer-executable instructions are executed, a method for constructing a digital maintenance platform as described in any one of claims 1-4 is implemented.

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