Analysis method of fmea data and electronic device

Abstract

Embodiments of the present application disclose a method for analyzing FMEA data and an electronic device. The method correlates FMEA data of a product based on an undesired event of the product to obtain correlation information including elements, element functions, failure chains, control requirements and document information; in response to a risk analysis request of the product, obtains to-be-analyzed data from the FMEA data according to the correlation information, and performs risk analysis on the to-be-analyzed data to obtain a risk analysis result including failure chain tracing, risk parameter dynamic analysis, whole-process correlation analysis and potential risk prediction, thereby effectively solving the problem of single analysis dimension caused by weak correlation of FMEA data, and further realizing deep fusion of multi-source data and intuitive presentation of risks, and improving the accuracy and efficiency of product risk decision-making.

Description

Technical Field

[0001] This application relates to the field of FMEA technology, and in particular to a method and electronic device for analyzing FMEA data. Background Technology

[0002] FMEA (Failure Mode and Effects Analysis) is a core tool used throughout the entire lifecycle of complex products to identify potential failure risks, assess their impact, and develop control measures. Product FMEA analysis generates massive amounts of multi-source heterogeneous data, covering failure chain information, risk parameters, control requirements, design / production execution data, and MSR (Monitoring and System Response) data.

[0003] However, the massive amounts of multi-source heterogeneous data generated by product FMEA analysis are fragmented, making it difficult for the product's risk decision-making process to efficiently penetrate data barriers, and making it difficult for data analysis to form a risk assessment result from a holistic perspective. Summary of the Invention

[0004] Based on this, this application provides a method and electronic device for analyzing FMEA data, which can improve the accuracy of risk identification and provide reliable data support and decision-making basis for the full life cycle safety management of complex products.

[0005] Firstly, this application provides a method for analyzing FMEA data, including: Based on the product's unexpected events, the product's FMEA data is correlated to obtain correlation information; In response to a product risk analysis request, the system retrieves the data to be analyzed from the FMEA data based on relevant information, performs risk analysis on the data to be analyzed, and obtains the product risk analysis results.

[0006] Secondly, this application also provides an FMEA data analysis apparatus, comprising: The association unit is used to associate the product's FMEA data based on the product's unexpected events to obtain association information; The response analysis unit is used to respond to the product's risk analysis request, obtain the data to be analyzed from the FMEA data based on the relevant information, perform risk analysis on the data to be analyzed, and obtain the product's risk analysis results.

[0007] Thirdly, embodiments of the present invention provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the FMEA data analysis method provided in the first aspect above.

[0008] Fourthly, embodiments of the present invention also provide a computer-readable storage medium storing a computer program that, when executed by a processor, causes the processor to perform the FMEA data analysis method provided in the first aspect.

[0009] Fifthly, embodiments of this application also provide a computer program product, including a computer program or instructions, wherein the computer program or instructions are executed by a processor using the FMEA data analysis method provided in the first aspect.

[0010] The FMEA data analysis method provided in this application unifies and correlates scattered FMEA data through unintended events of the product, constructing a structured correlation information centered on unintended events. This enables the correlation of isolated elements, element functions, failure chains, control requirements, design documents, production documents, and MSR modules from multiple sources. Consequently, when responding to risk analysis requests, the method can accurately extract the data to be analyzed and perform in-depth analysis through the correlation information. This effectively solves the problems of incomplete risk views, difficulty in root cause identification, and decision-making lag caused by data fragmentation and missing correlations in FMEA data analysis. It realizes the transformation from single-parameter statistics to multi-dimensional integrated analysis, significantly improving the accuracy of product risk identification, the completeness of traceability, and the pertinence of control measures. In turn, it provides reliable data support and decision-making basis for the full life cycle safety management of complex products. Attached Figure Description

[0011] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0012] Figure 1 A first flowchart illustrating the FMEA data analysis method provided in this application embodiment; Figure 2 A second flowchart illustrating the FMEA data analysis method provided in this application embodiment; Figure 3 A schematic block diagram of an FMEA data analysis device provided in an embodiment of this application; Figure 4A schematic block diagram of an electronic device provided in an embodiment of this application. Detailed Implementation

[0013] 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.

[0014] It should be understood that, when used in this specification and the appended claims, the terms "comprising" and "including" indicate the presence of the described features, integrals, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0015] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the scope of the application. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0016] It should also be further understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0017] Furthermore, in this application, unless otherwise explicitly specified or limited in the embodiments, the terms "installation," "connection," "joining," and "fixing" appearing in the embodiments should be interpreted broadly. For example, a connection can be a fixed connection, a detachable connection, or an integral part; it can also be a mechanical connection, an electrical connection, etc. Of course, it can also be a direct connection, or an indirect connection through an intermediate medium, or it can be the internal communication between two components, or the interaction between two components. Those skilled in the art can understand the specific meaning of the above terms in this application based on the specific implementation.

[0018] For ease of understanding, some key terms in this embodiment are explained below.

[0019] Failure Cause (FC) is the fundamental or direct factor that leads to the occurrence of a failure mode.

[0020] A failure mode (FM) is a specific way in which a product or its components may lose functionality or degrade performance. For example, excessive power supply output voltage can be considered a failure mode.

[0021] Failure Effect (FE) is the consequence of a failure mode on a product, system, user, or environment.

[0022] A failure chain (FC-FM-FE) is a logical sequence or a data structure that includes the cause of failure, the failure mode, and the impact of failure, to ensure a comprehensive description of a single failure event.

[0023] An undesired event (UE) is an event caused by one or more failure modes and their potential causes that negatively impacts product functionality or system performance.

[0024] A System Undesired Event (SUE) is a serious event that occurs at the system level and is caused by the failure modes and causes of one or more components / subsystems, resulting in a deviation of the overall system functionality, performance, or safety objectives from the expected state.

[0025] Functional networks are a graphical or structured representation used to show the relationships, dependencies, or execution order among the various functions (FUNCs) in a product.

[0026] End-of-Line Defect (EOL-DEF) refers to non-conformities found during final inspection at the end of the production line. Its core objective is to identify defects and ensure that products meet quality standards.

[0027] In related technologies, Failure Mode and Effects Analysis (FMEA) covers the entire chain from product architecture elements and functional definitions to failure causes, failure modes, and failure effects, and extends to multiple links such as control requirements, design documents, production processes, and monitoring and system responses. This results in multi-source heterogeneous FMEA data that includes failure chain information, risk parameters, control requirements, design execution data, and monitoring response data.

[0028] Currently, multi-source heterogeneous FMEA data can be stored by establishing independent databases or document management systems, such as independent failure networks, requirement databases, design document databases, and monitoring systems, to store the data generated by failure mode and impact analysis, and to use preset tags to mark product elements, functions, failure events, etc.

[0029] When risk analysis is required, isolated data points can be sorted and filtered based on a pre-set risk parameter calculation model, such as the product of severity, occurrence, and detectability. Alternatively, relevant design documents and production records can be manually reviewed to generate an assessment report on product quality and safety, thereby guiding subsequent design optimization or production adjustments.

[0030] However, there are logical breaks in the multi-source heterogeneous FMEA data, resulting in fragmented FMEA data. It is difficult to intuitively and systematically present the deep logical relationship between the failure link and the data of each link. As a result, the product risk decision-making process cannot efficiently penetrate the data barrier, and data analysis is difficult to form a risk assessment result from a global perspective. This leads to low risk decision-making efficiency and the inability to capture the product risk change trend in a timely manner.

[0031] To address this, this application provides a method for analyzing FMEA data. It unifies and correlates fragmented FMEA data through unintended events in the product, constructing a structured, interconnected information framework centered on these unintended events. This allows for the correlation of isolated elements, element functions, failure chains, control requirements, design documents, production documents, and MSR modules from multiple sources. Consequently, when responding to risk analysis requests, the method accurately extracts the data to be analyzed and performs in-depth analysis based on this interconnected information. This effectively solves the problems of incomplete risk views, difficulty in root cause identification, and delayed decision-making caused by data fragmentation and missing correlations in FMEA data analysis. It achieves a shift from single-parameter statistics to multi-dimensional integrated analysis, significantly improving the accuracy of product risk identification, the completeness of traceability, and the targeting of control measures. Ultimately, this provides reliable data support and decision-making basis for the full lifecycle safety management of complex products.

[0032] The FMEA data analysis method provided in this application can be applied to terminal devices. This method is executed through application software installed on the terminal device. The terminal device can be an internet-enabled device, such as a desktop computer, laptop computer, tablet computer, or mobile phone.

[0033] It should be noted that the application scenarios described in the following embodiments of this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided by the embodiments of this application. As those skilled in the art will know, with the emergence of new application scenarios, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems.

[0034] The analysis method for the FMEA data provided in this application will be described in detail below.

[0035] like Figure 1As shown, the method includes the following steps S110~S120.

[0036] S110. Based on the product's unexpected events, correlate the product's FMEA data to obtain correlation information; S120. In response to the product's risk analysis request, obtain the data to be analyzed from the FMEA data based on the associated information, perform risk analysis on the data to be analyzed, and obtain the product's risk analysis results.

[0037] In this embodiment, an Undesired Event (UE) can be understood as a system-level failure event that may occur during product operation and needs to be avoided or addressed. An Undesired Event is an abstract summary of the functional failure chain (failure cause - failure mode - failure impact).

[0038] FMEA data can be understood as a collection of multi-source heterogeneous data covering the entire product lifecycle. FMEA data can include the product's architectural elements, the functions carried by the elements, complete failure chain data, control requirements data, design documents, production documents, and monitoring and system MSR data.

[0039] In the process of associating product FMEA data, this application can use the unwanted event label (UE-ID) as the core penetrating node to build a multi-level ID association model, thereby breaking down multi-source data barriers and integrating isolated FMEA data into structured association information, providing a unified data view and precise targeting support for subsequent global risk analysis.

[0040] Specifically, the unwanted event ID is pre-determined, and then associated with the product element ID to clarify the product architecture carrier to which the failure event belongs. The element ID is then associated with the function ID (FUNC-ID), binding the specific function carried by the carrier. Next, the function ID is linked with the failure cause ID (FC-ID), failure mode ID (FM-ID), and failure impact ID (FE-ID) in the failure chain to form the main logical link of element-function-failure chain-unwanted event. Finally, using the failure chain ID as the link, it is further associated downwards with the control requirement ID, design document ID, production document ID, and MSR module ID, and attribute tags (such as risk level, scenario type, control status, etc.) are bound to each level ID, thereby obtaining the associated information.

[0041] For example, in the automatic emergency braking system (AEBS) of intelligent connected vehicles, undesirable events (such as UE-ID: UE_001, the vehicle cannot decelerate effectively) are associated with the brake control unit (such as ECU_01), and then with the hydraulic build-up function (such as FUNC_Hyd_02). This can then be associated with specific failure causes such as solenoid valve sticking (FC-ID), failure mode slow pressure build-up (FM-ID), and failure impact extended braking distance (FE-ID), and the corresponding design drawing number, manufacturing process documents, and real-time pressure monitoring sensor data are also associated simultaneously.

[0042] A risk analysis request can be understood as a risk assessment instruction initiated by a user or upper-level system for a specific product or scenario. Risk analysis requests can include failure chain tracing analysis requests, dynamic risk parameter analysis requests, end-to-end correlation analysis requests, or potential risk prediction analysis requests, etc.

[0043] In the process of obtaining the data to be analyzed from FMEA data based on the associated information, this application can utilize a constructed multi-level ID association model to perform bidirectional penetration queries based on the type of risk analysis request and through the core unwanted event ID. This allows for the accurate extraction of data subsets strongly related to the analysis target, realizing the transformation from static data association to dynamic risk assessment. Furthermore, it can output in-depth insights for different analysis dimensions, significantly improving the accuracy and efficiency of product risk identification and analysis, and avoiding the omission of critical failure paths due to data loss or limited perspective.

[0044] Specifically, if the risk analysis request is a failure chain tracing request, then complete causal chain data and historical fault records can be extracted along the path of UE-ID, element ID, FUNC-ID and failure chain ID; if the risk analysis request is a dynamic analysis request, then real-time risk parameters (RPN), control measure implementation data and MSR response data can be extracted.

[0045] Furthermore, after obtaining the data to be analyzed from the FMEA data and conducting risk analysis on the data to be analyzed, high-frequency failure modes and associated failure causes can be identified, the effectiveness of control measures can be assessed, the impact of each link on risk can be quantified, or machine learning algorithms can be used to predict the probability and time-series trend of potential failures.

[0046] For example, when a request for dynamic analysis of risk parameters for the AEBS system is received, the RPN value under the current operating condition, the test data after the most recent design change, and the actual braking pressure curve fed back by the MSR module are automatically extracted based on the association information of UE_001. Risk identification analysis is then performed to determine that the design parameters have been optimized, but the MSR data shows that the pressure build-up rate is still lower than the threshold in the low temperature environment. This indicates that the existing control measures are not effective enough in the specific scenario, and a risk analysis result containing the key finding of braking response lag in the low temperature environment can be generated.

[0047] In this application, by linking the product's FMEA data with undesirable events, the problems of scattered FMEA data and broken logical links are solved. This makes FMEA data analysis no longer an isolated parameter calculation, but a full-link penetration and multi-dimensional integration around core risk events. This not only significantly enhances the traceability of data, but also dynamically captures risk change trends. It can also provide decision-makers in different roles with intuitive and accurate risk management basis, effectively reducing the failure risk of complex products throughout their entire life cycle.

[0048] In some embodiments, in step S110, the FMEA data of the product is correlated based on the undesirable events of the product to obtain correlation information, including: obtaining FMEA data; the FMEA data includes product elements, element functions, failure chains, undesirable events, control requirements, design documents, production documents, and MSR modules; and associating at least one of the elements, element functions, failure chains, control requirements, design documents, production documents, and MSR modules with undesirable events to obtain correlation information.

[0049] In this embodiment, during the process of acquiring FMEA data, the product's architectural elements, the functions carried by the elements, complete failure chain data, control requirements data, design documents, production documents, and monitoring and system MSR data can be obtained from the pre-built ID-based data closed-loop system.

[0050] Specifically, this application can obtain element IDs and function IDs (FUNC-ID) by parsing the design database, extract unwanted event IDs (UE-ID) and their associated failure cause IDs (FC-ID), failure mode IDs (FM-ID), and failure effect IDs (FE-ID) from the failure event database, and pull the corresponding design document IDs, production document IDs, and MSR module operation logs from the document management system and vehicle monitoring system.

[0051] As an example, for the Automatic Emergency Braking System (AEBS) of intelligent connected vehicles, the elements can be radar sensors, the function of which is target distance detection, the failure chain is signal loss leading to false braking, the undesirable event is unexpected vehicle deceleration, the control requirement is to increase redundancy verification, the design document is radar installation location drawings, the production document is EOL detection records, and the MSR module is a real-time obstacle recognition algorithm.

[0052] Meanwhile, during the process of acquiring FMEA data, this application can perform standardized cleaning and format adaptation of FMEA data, unify the identification specifications of data in each dimension, thereby obtaining a structured FMEA dataset, which significantly improves the usability and consistency of the data and effectively avoids analytical bias caused by missing data or inconsistent formats.

[0053] In the process of associating multi-source data with unwanted events, this application can use unwanted events as core penetration nodes and utilize a unified ID mapping mechanism to logically connect scattered elements, functions, failure chains and other supporting data to form association information with clear direction.

[0054] Specifically, this application can establish a mapping relationship between the unique identifiers of each data (such as element ID, FUNC-ID, FC / FM / FE-ID, document ID, MSR module ID) and the unique identifiers of unwanted events (UE-ID) to obtain associated information.

[0055] Among them, the undesirable events can be abstracted and summarized into the failure chain triplet (FC-FM-FE) associated with FUNC-ID, and used as an index of the core failure events that the product ultimately needs to avoid or deal with.

[0056] Meanwhile, when setting up the association, this application can first establish the attribution relationship between UE-ID and element ID according to the logical order of the FMEA seven-step method, and then extend it downward to FUNC-ID and specific failure chain ID, and then horizontally associate control requirements, design and production documents and MSR modules, thereby enabling instantaneous penetration of the entire upstream and downstream data from a single risk event, solving the problems of data fragmentation and logical link breakage in traditional technologies.

[0057] The combined use of elements, element functions, and unforeseen events can clarify the physical carrier and functional background of failure events; the collaboration between control requirements, design documents, production documents, and the MSR module with unforeseen events can provide comprehensive contextual support from preventive design to real-time monitoring, thereby providing precise data targeting for subsequent failure chain tracing, dynamic analysis of risk parameters, and visualization, effectively supporting risk decision-making from a global perspective.

[0058] For example, when dealing with unexpected events such as unintended vehicle deceleration, the UE-ID can be bound to the radar sensor element ID, the target distance detection FUNC-ID, and the failure cause ID of signal loss, while also associating the corresponding radar installation location drawing ID and the real-time obstacle recognition algorithm module ID.

[0059] In addition, when product design changes or new MSR anomaly records are generated, the associated information can be dynamically updated to ensure the real-time nature of the data link.

[0060] In this application, by acquiring FMEA data from elements, functions, failure chains, control requirements, design and production documents, and the MSR module, and explicitly linking them with unforeseen events, not only are the carriers (elements) and mechanisms (functions and failure chains) of failure events clearly identified, but preventive measures (control requirements, design documents), process control basis (production documents), and post-event monitoring methods (MSR module) are also integrated simultaneously. This creates a logical closed loop for data that was originally scattered across different business systems. Consequently, when analyzing a specific unforeseen event, the corresponding design basis, production records, and real-time monitoring status can be retrieved instantly, thereby comprehensively assessing the root cause and scope of impact of the risk. This lays a solid data foundation for generating accurate failure chain tracing reports, dynamically evaluating the effectiveness of control measures, and realizing hierarchical dynamic risk visualization, significantly improving the completeness, accuracy, and decision support capabilities of FMEA data analysis.

[0061] In some embodiments, at least one of the elements, element functions, failure chains, control requirements, design documents, production documents, and MSR modules is associated with an undesirable event to obtain association information, including: extracting a first label for the element, a second label for the element function, a third label for the failure chain, a fourth label for the undesirable event, a fifth label for the control requirements, a first document label for the design document, a second document label for the production document, and a sixth label for the MSR module from FMEA data; and associating the first label, second label, third label, fifth label, first document label, second document label, and sixth label with the fourth label to obtain association information.

[0062] In this embodiment, the first tag, second tag, third tag, fourth tag, fifth tag, first document tag, second document tag, and sixth tag can be obtained by parsing the metadata fields in the pre-built ID-based data closed-loop system.

[0063] The first tag corresponds to the product's architectural elements, which includes a unique identifier after element ID is generated, used to identify the physical or logical carrier to which the failure event belongs.

[0064] The second label corresponds to the specific function carried by the element, which includes the Element Function ID (FUNC-ID), used to characterize the element's behavior definition in the system.

[0065] The third label corresponds to the failure chain, which includes a combination of failure cause (FC), failure mode (FM), and failure effect (FE) identifiers to describe the failure propagation path.

[0066] The fourth label corresponds to the unexpected event and can be understood as an abstract summary identifier of the failure chain triple. The fourth label can serve as the core hub of the association.

[0067] The fifth label corresponds to control requirements, which originates from the control measure requirement identifier (REQ-ID) determined after risk analysis.

[0068] The first document tag corresponds to the design document (DES) number or name in the design phase, and the second document tag corresponds to the process / production document (PRO) number or name in the production phase. The first and second document tags can be used as a basis for tracing data sources and execution.

[0069] The sixth tab corresponds to the Monitoring and System Response (MSR) module, which is used to associate real-time monitoring and response strategies.

[0070] For example, when processing power module data of the Automatic Emergency Braking System (AEBS) of intelligent connected vehicles, the element IDLEM_001 can be read from the database as the first label, the function IDFUNC_VOLT_MONITOR as the second label, the failure chain combination FC_OVERVOLT-FM_SENSOR_DRIFT-FE_BRAKE_FAIL as the third label, and the unwanted event IDUE_BRAKE_LOSS can be extracted as the fourth label.

[0071] Specifically, in the process of associating the first tag, second tag, third tag, fifth tag, first document tag, second document tag, and sixth tag with the fourth tag, this application can form a network data structure with the fourth tag (undesirable event tag) as the core node. Then, various tags such as elements, functions, failure mechanisms, control measures, execution documents, and monitoring responses can be attached to the fourth tag of the core failure event. This allows the construction of a full-dimensional association model of undesirable event-elements-functions-failure chain-control-documents-monitoring, enabling a global perspective that penetrates from a single risk point to all lifecycle related data. This effectively solves the technical problems of fragmented and uncoordinated product FMEA data.

[0072] Among them, the association between the first and fourth labels can identify the physical carrier of the failure event; the association between the second and fourth labels can reveal the specific function undertaken by the carrier and its failure background; the association between the third and fourth labels can restore the complete failure transmission path from cause to effect; the association between the fifth and fourth labels can lock in the control strategy for undesirable events; the association between the first and second document labels and the fourth label can realize reverse tracing from risk events to design source and production execution; and the association between the sixth and fourth labels can break down the data barriers between static analysis and dynamic monitoring.

[0073] For example, this application can use the extracted UE_BRAKE_LOSS (fourth tag) as the primary key, create an index in the associated database, aggregate all records containing the event ID, so that FUNC_VOLT_MONITOR (second tag), FC_OVERVOLT (third tag) and the related document tag DES_PWR_005 all directly point to the unwanted event.

[0074] In this application, by introducing a tagging mechanism and using an association model centered on undesirable events, deep integration of multi-source FMEA data can be achieved. Specifically, multi-dimensional tags covering elements, functions, failure chains, control requirements, design and production documents, and MSR modules are extracted from FMEA data, and each tag is uniformly associated with the tag of undesirable events. This allows data scattered across different business processes to be seamlessly connected, greatly improving data retrieval efficiency and the comprehensiveness of analysis.

[0075] Meanwhile, this application can also lay a data foundation for subsequent application of machine learning algorithms for risk prediction, knowledge graph construction and hierarchical dynamic visualization, effectively overcoming the shortcomings of single dimension, weak correlation and low degree of visualization in FMEA data analysis, and significantly enhancing the risk management capability and decision support level of the entire product life cycle.

[0076] In some embodiments, associating the first tag, second tag, third tag, fifth tag, first document tag, second document tag, and sixth tag with the fourth tag to obtain association information includes: associating the fourth tag with the first tag to obtain first sub-association information; associating the first tag with the second tag based on the first sub-association information to obtain second sub-association information; associating the second tag with the third tag based on the second sub-association information to obtain third sub-association information; and associating the third tag with the fifth tag, first document tag, second document tag, and sixth tag based on the third sub-association information to obtain association information.

[0077] In this embodiment, associating the fourth label with the first label can establish a top-level mapping relationship between failure events and carrying elements, thereby clarifying the product architecture level to which the unwanted event belongs.

[0078] Specifically, this application can determine the specific hardware module or software component that caused the unexpected event by searching the unified ID association database, using the fourth tag as the index key, and matching the first tag bound to it.

[0079] For example, in the Automatic Emergency Braking System (AEBS) of a smart connected vehicle, if the undesirable event is that the vehicle cannot brake in time, its corresponding fourth tag is UE-001. Then, the first tag EL-Brake (brake control unit) can be located through the association operation, indicating that the failure event directly belongs to the brake control unit.

[0080] Meanwhile, in the process of associating the first tag with the second tag based on the first sub-association information, this application can extract all second tags belonging to the first tag from the function library according to the logical architecture of the functions carried by the product elements, and establish a binding relationship between the two. This enables the penetration from static carrier to dynamic behavior, clarifies the specific functional links involved in the undesirable event, and allows the risk analysis to be refined to the functional granularity, avoiding analysis bias caused by the ambiguity of the element function definition.

[0081] Furthermore, in the process of associating the second label with the third label based on the second sub-association information, this application can extend the analysis perspective from the normal functional state to the abnormal failure state to identify the specific failure chain caused by the specific functional abnormality. Specifically, it can retrieve the failure mode library bound to it, extract the corresponding FC-ID, FM-ID and FE-ID as the third label, and record its causal transmission path, thereby revealing the internal mechanism of the failure and providing structured data support for subsequent root cause tracing and impact assessment.

[0082] Furthermore, in the process of associating the third tag with the fifth tag, the first document tag, the second document tag, and the sixth tag based on the third sub-association information, this application can deeply integrate the failure chain with the full lifecycle management and control data.

[0083] Specifically, this application can use the third tag as a hub to horizontally pull the corresponding fifth tag (such as the requirement ID for adding redundant sensors), vertically link the first document tag (such as the document ID of the braking system schematic diagram.pdf) and the second document tag (such as the document ID of the assembly operation instruction.doc), and synchronously associate the sixth tag (such as the module ID of the MSR pressure monitoring algorithm) to obtain the associated information.

[0084] In this application, the spatial attribution of risk is first established by the association between the fourth label and the first label. Then, the behavioral carrier of risk can be identified by the association between the first label and the second label. Subsequently, the evolution mechanism of risk can be revealed by the association between the second label and the third label. Finally, based on the association between the third label and the fifth, first, second, and sixth labels, comprehensive coverage of risk management measures can be achieved. This not only ensures the logical rigor and causal consistency of data association, but also includes static data mapping and embeds the dynamic path of failure propagation. Thus, when conducting risk analysis on products, it is possible to quickly penetrate from macroscopic undesirable events to microscopic design parameters and monitoring strategies, significantly improving the accuracy of root cause localization and the efficiency of risk decision-making.

[0085] In some embodiments, in step S120, data to be analyzed is obtained from FMEA data based on the association information, and risk analysis is performed on the data to be analyzed to obtain the risk analysis results of the product. This includes: if the risk analysis request is a failure chain tracing analysis request for the product, extracting element information, element function information, failure chain information, unexpected event information, historical fault data, and MSR anomaly records corresponding to the unwanted event from the FMEA data based on the association information; extracting the target failure mode, target failure cause, and interlocking risk of the unwanted event from the element information, element function information, failure chain information, and unexpected event information based on the historical fault data and MSR anomaly records; and generating a failure chain tracing report for the product based on the unwanted event, target failure mode, target failure cause, and interlocking risk.

[0086] In this embodiment, the triggering condition for a risk analysis request can be the detection of a critical functional anomaly or the commencement of a periodic safety review phase. A failure chain tracing analysis request can be understood as an instruction to analyze the product's failure chain.

[0087] Feature information can be understood as the specific attributes of the physical or logical components that carry failure events. Feature information includes feature ID, name, and hierarchical position in the system architecture.

[0088] Element function information can be understood as a description of the specific functions performed by a product element and its function ID (FUNC-ID). Element function information is used to clarify the business scenario in which the failure occurs.

[0089] Failure chain information can be understood as complete transmission path data consisting of failure cause (FC), failure mode (FM), and failure effect (FE).

[0090] Unexpected event information can be understood as an abstract summary identifier (UE-ID) of the failure chain.

[0091] Historical fault data can include after-sales maintenance records throughout the product's entire lifecycle and a database of bench test failure cases. Historical fault data can also include statistics on specific fault phenomena and root causes that have occurred in the past.

[0092] MSR anomaly logs can include the Monitoring and System Response Module (FMEA-MSR). MSR anomaly logs can record real-time monitoring data such as diagnostic fault codes (DTCs), sensor signal over-limits, or control strategy interventions triggered during vehicle operation.

[0093] For example, when a tracing request is initiated for the Automatic Emergency Braking System (AEBS) of an intelligent connected vehicle, the system uses the UE-ID of the unexpected event triggered by the braking mishap as the core index. Through a six-layer association model, it quickly retrieves the corresponding radar sensor element information, ranging function information, and related FC-FM-FE failure chain entries. Simultaneously, it pulls the historical fault work orders of the model in low-temperature environments over the past year, as well as the radar signal loss anomaly logs recorded by the on-board diagnostic system, to provide comprehensive support for accurately locating the root cause.

[0094] The target failure mode can be understood as the specific form of functional loss that has the highest probability of occurrence or the most severe impact in the current analysis scenario, after dual verification by historical data and real-time monitoring data.

[0095] The cause of target failure can be understood as the fundamental physical mechanism or logical error that leads to the failure mode of the target.

[0096] Interlocking risks can be understood as the risks of secondary disasters or systemic chain reactions caused by the failure mode of the target.

[0097] In this embodiment, during the extraction of the target failure mode, target failure cause, and interlocking risk of the unwanted event, the present application can use a weighted matching algorithm to perform semantic comparison and confidence calculation on the high-frequency failure features in the historical failure data and the time-series abnormal patterns in the MSR abnormal records with the pre-stored failure chain information.

[0098] Specifically, this application can statistically analyze the frequency of occurrence of each failure cause in historical fault data, and combine the causal correlation strength between abnormal signals and failure modes in MSR anomaly records to select failure combinations with confidence levels exceeding a preset threshold as the target results.

[0099] For example, if MSR anomaly logs show that a camera frequently outputs noise signals under strong light, and if 80% of the historical fault data shows that false braking was caused by image recognition errors, then insufficient signal-to-noise ratio of the image sensor is identified as the cause of the target failure, and failure of the environmental perception function is identified as the target failure mode. Furthermore, the risk of a rear-end collision caused by unexpected vehicle deceleration can be deduced. Historical fault data provides statistical prior probabilities, while MSR anomaly logs provide real-time status evidence, thus significantly improving the accuracy of identification and effectively eliminating low-probability or non-current interference.

[0100] A failure chain tracing report is a structured analytical document or visual data collection. It can effectively shorten the cycle from problem discovery to corrective action, improving the efficiency of product safety iteration. Specifically, a failure chain tracing report may include the target failure mode, the target failure cause, interlocking risks, a description of the original unintended event, relevant elements and functional context, historical failure statistics charts, and MSR (Mean Situational Risk Level) anomaly waveform evidence.

[0101] Specifically, in the process of generating a product failure chain tracing report, this application can use an automatic filling preset template to form a standard format file that includes an event overview, root cause analysis, impact assessment and improvement suggestions. Alternatively, it can generate an interactive electronic report to support users to click and drill down to view the underlying raw data.

[0102] For example, the generated report will clearly state that the main root cause of the AEBS system's mis-triggered braking (i.e., the unexpected event) is blinding due to strong light from the camera (i.e., the cause of target failure), leading to incorrect obstacle distance calculation (i.e., the target failure mode), which in turn triggers unexpected braking, increasing the risk of rear-end collisions (i.e., the interlocking risk). The report will also include a distribution chart of the number of related failure cases and a time series chart of abnormal signals. This report directly serves R&D improvement and quality decisions, allowing engineers to grasp the full picture of the failure without manually sifting through scattered data.

[0103] In this application, by utilizing historical failure data and MSR anomaly records as dynamic evidence, static FMEA correlation information is verified and focused, achieving a precise leap from a generalized failure list to specific target failure modes and target failure causes. Historical failure data can provide long-term statistical patterns, while MSR anomaly records can provide short-term real-time status, thereby generating highly reliable failure chain tracing reports, significantly improving the root cause localization capability and risk management level of complex products when facing specific undesirable events.

[0104] In some embodiments, the risk analysis results also include effectiveness analysis information for controlling the product's functional elements and failure chains; in step S120, data to be analyzed is obtained from FMEA data based on the correlation information, and risk analysis is performed on the data to be analyzed to obtain the product's risk analysis results, including: if the risk analysis request is a dynamic analysis request for the product's risk parameters, the RPN parameters, control information, and MSR response information corresponding to the unwanted events are extracted from FMEA data based on the correlation information; and effectiveness analysis information for controlling the product's functional elements and failure chains is generated based on the RPN parameters, control information, and MSR response information.

[0105] In this embodiment, the risk parameter dynamic analysis request can be understood as an instruction to dynamically analyze the RPN parameter of the product. The RPN (Risk Priority Number) parameter can be understood as a numerical indicator used to quantify the risk assessment level. It includes the product of scores of three dimensions: severity (S), occurrence (O), and detectability (D), i.e., RPN = S × O × D.

[0106] Control information can be understood as the specific implementation plan and status record of design changes, process optimizations or management strategies formulated for specific failure modes.

[0107] MSR response information can be understood as real-time or historical data from the Monitoring and System Response (MSR) module, reflecting the system's actual performance in monitoring and responding to failures during operation. MSR response information may include sensor alarm frequency, system intervention times, and fault recovery time.

[0108] Specifically, in the process of extracting RPN parameters, control information and MSR response information corresponding to unwanted events from FMEA data, this application can achieve the core association information based on unwanted event ID (UE-ID). Specifically, the UE-ID can be used to penetrate and associate with specific element ID, function ID (FUNC-ID) and failure chain ID, thereby accurately locating and aggregating multi-source heterogeneous data scattered in design documents, production records and MSR systems.

[0109] For example, when the system receives a request for dynamic analysis of risk parameters for an undesirable event such as lane keeping function failure in a certain intelligent driving system, it can lock the UE-ID corresponding to the undesirable event, and then retrieve the current RPN value of the function from the database (e.g., S=8, O=4, D=5, RPN=160), extract the control information of the most recent software upgrade (e.g., optimized camera image recognition algorithm), and simultaneously obtain the number of lane departure warning triggers and the frequency of manual intervention recorded by the MSR module within one month after the upgrade as MSR response information. This ensures the integrity and consistency of the analysis data, avoids omissions or biases caused by manual data collection, and significantly improves the objectivity and timeliness of risk analysis.

[0110] Effectiveness analysis information can be understood as a quantitative evaluation result used to characterize the actual effectiveness of implemented control measures in reducing failure risk and blocking failure transmission paths.

[0111] In the process of generating effectiveness analysis information for controlling the functional elements and failure chains of a product, this application can cross-validate and logically compare the static RPN parameter change trend with the dynamic MSR response information to obtain effectiveness analysis information. This not only verifies the effect of a single control measure, but also reveals the complex coupling effect between functional elements and failure chains, ensuring that risk control strategies are truly implemented and effective.

[0112] Specifically, this application can first calculate the RPN difference before and after the implementation of control measures to determine whether the theoretical risk level has decreased. Then, it can analyze whether the key indicators in the MSR response information (such as alarm rate, false alarm rate, and system intervention success rate) show the expected improvement trend. If the RPN value decreases and the frequency of abnormal events monitored by the MSR decreases simultaneously, the control is deemed effective. If the RPN value decreases but the MSR data shows that the frequency of abnormal events has not decreased or has even increased, it is determined that there is a paper compliance risk, that is, the theoretical assessment does not match the actual performance, and further investigation is needed to identify blind spots in the detection mechanism or deviations in the implementation of control measures.

[0113] For example, if the data shows that the RPN value drops from 160 to 100 after the algorithm optimization (mainly because the detection rate D drops from 5 to 3), but the MSR response information shows that the number of lane departure warnings triggered has not decreased significantly, or even that false alarms have surged due to the algorithm's high sensitivity, then the generated effectiveness analysis information will mark the control measure as partially effective but with new risks, and indicate that the detection threshold needs to be recalibrated.

[0114] In addition, during the process of generating effectiveness analysis information for controlling the functional elements and failure chains of a product, this application can also use a preset evaluation model or machine learning algorithm to perform weighted processing and pattern recognition on the input multidimensional data, and automatically output an analysis report containing effectiveness level, potential problem points and optimization suggestions, i.e. effectiveness analysis information. This can provide core basis for subsequent risk visualization and early warning linkage, enabling managers to intuitively identify whether the risks in the corresponding links have been substantially controlled or still have hidden dangers, thereby significantly improving the reliability and safety of the product throughout its entire life cycle.

[0115] In this application, by deeply integrating the extracted RPN parameters, control information, and MSR response information, it is possible not only to quantitatively assess the actual contribution of design changes or process optimizations to reducing the probability of failure, but also to verify the true effectiveness of detection measures using real-time MSR monitoring data. Furthermore, by cross-validating RPN trends and MSR response data, it is possible to effectively identify the discrepancies between theoretical analysis and actual operation, avoiding misjudgments caused by data silos. This allows the generated effectiveness analysis information to directly point to the product's functional elements and failure chains, clarifying the effects of control measures on specific functional carriers. This provides precise decision support for continuous optimization of the FMEA closed loop, ensuring that product risk management shifts from passive response to proactive prevention and dynamic optimization.

[0116] In some embodiments, the risk analysis results also include the correlation and impact between various stages in the product FMEA analysis; in step S120, based on the correlation information, the data to be analyzed is obtained from the FMEA data, and risk analysis is performed on the data to be analyzed to obtain the risk analysis results of the product, including: if the risk analysis request is a full-process correlation analysis request of the product FMEA, based on the correlation information, the failure mode information, control requirement information, design and production information and monitoring response information corresponding to the unwanted events are extracted from the FMEA data; based on the failure mode information, control requirement information, design and production information and monitoring response information, the correlation and impact between various stages in the product FMEA analysis are generated.

[0117] In this embodiment, the end-to-end correlation analysis request can be understood as an instruction to quantitatively assess the interaction relationships between various stages in the entire lifecycle of FMEA analysis. The end-to-end correlation analysis request can be triggered based on pre-built correlation information centered on Undesirable Event ID (UE-ID), and can be used to penetrate elements, functions, failures, requirements, design / production, and monitoring data.

[0118] Failure mode information can be understood as specific failure performance data bound to a function ID (FUNC-ID). Failure mode information may include the failure mode ID (FM-ID), failure description, and frequency of occurrence.

[0119] Control requirements information can be understood as risk control measures formulated for failure modes, along with their corresponding requirement IDs (REQ-IDs) and implementation status.

[0120] Design and production information may include design parameter change records associated with the design document ID (DES-ID) and process execution data or defect records (such as EOL-DEF) associated with the production document ID (PRO-ID).

[0121] The monitoring response information can include real-time monitoring data associated with the MSR module ID and historical abnormal response records.

[0122] Specifically, in the process of extracting failure mode information, control requirement information, design and production information, and monitoring response information corresponding to unwanted events from FMEA data, this application can perform bidirectional penetration queries based on UE-ID. It can first locate the functional carrier (element ID + FUNC-ID) to which the unwanted event belongs, and then obtain the failure mode information corresponding to the FM-ID by following the failure chain downwards. At the same time, it can horizontally pull the control requirement information corresponding to the REQ-ID associated with the FM-ID, and further extend to the design, production, and monitoring data carried by the DES-ID, PRO-ID, and MSR module ID at the execution layer. This can effectively avoid analysis bias caused by missing data.

[0123] For example, when a full-process correlation analysis request is initiated for an undesirable event of brake failure in a certain intelligent driving system, information on the hydraulic pump pressure insufficiency failure mode corresponding to the undesirable event of brake failure, related information on the need to increase the redundancy control of pressure sensors, the corresponding design drawing version changes and production line assembly torque data, as well as pressure monitoring waveform data during vehicle operation can be extracted.

[0124] Correlation can be understood as the degree of logical coupling between data at each node in the entire product FMEA process. Specifically, correlation can be the statistical correlation between design parameter changes and failure mode occurrence rates, or the causal chain between the implementation of control measures and the effectiveness of monitoring responses.

[0125] Impact can be understood as the quantitative contribution weight of a change in the state of a certain link to the overall risk level (such as RPN value) or the performance of other links.

[0126] In generating the correlation and influence between various stages in the product FMEA analysis, this application can use multi-dimensional data fusion analysis and causal inference algorithms to transform the product FMEA process into a quantitative influence network. This allows for the precise identification of key bottlenecks (such as critical design points or weak control points) that lead to risk escalation, providing a basis for subsequent optimization of resource allocation and adjustment of control strategies. This significantly enhances the guiding value of FMEA analysis for the entire process of risk management.

[0127] As an example, this application can first use failure mode information and monitoring response information to identify the deviation between actual failures and theoretical models. Then, design and production information is used as independent variables and control requirements information is used as constraints to construct an impact network model of design / production changes, failure mode evolution and control effect feedback. Finally, by calculating the correlation coefficient or sensitivity index between each node, a quantitative correlation matrix and impact score are output.

[0128] As another example, when the welding process parameters (design and production information) of a batch of products deviate from the standard value by 5%, the probability of the circuit breaker failure mode (failure mode information) increases by 20%. However, the existing periodic inspection and control requirements (control requirements information) fail to intercept this risk in time, which ultimately manifests as a high-frequency anomaly in MSR monitoring (monitoring response information). At this point, it can be calculated that the direct impact of the welding process on the current risk is high, and the correlation between the periodic inspection process and failure interception is weak, thereby generating a specific correlation report and impact ranking.

[0129] In some embodiments, the risk analysis results also include the product's potential risks; in step S120, the data to be analyzed is obtained from the FMEA data according to the association information, and risk analysis is performed on the data to be analyzed to obtain the product's risk analysis results, including: if the risk analysis request is a potential risk prediction analysis request for the product, the historical monitoring data and real-time monitoring data of the MSR corresponding to the undesirable event are extracted from the FMEA data according to the association information; based on the historical monitoring data and real-time monitoring data of the MSR, the potential risks of the failure mode corresponding to the undesirable event are predicted; the potential risks include failure probability, failure impact range, and time series trend.

[0130] In this embodiment, the potential risk prediction analysis request can be understood as an instruction automatically triggered by the user or system to predict the potential failure risk of the product in the future but which has not yet occurred.

[0131] MSR historical monitoring data can be understood as records of product operating status collected by monitoring and the MSR module over a historical period and stored in a database. This MSR historical monitoring data may include historical fault codes, sensor reading sequences, actuator action logs, etc.

[0132] MSR real-time monitoring data can be understood as the product operation flow data collected and transmitted in real time by the MSR module at the current moment or within the most recent time window.

[0133] Specifically, in the process of extracting MSR historical monitoring data and MSR real-time monitoring data corresponding to unwanted events from FMEA data, this application can determine the target unwanted event by using pre-constructed association information with the unwanted event ID (UE-ID) as the core, locate the bound MSR module ID in the unified ID association database using the UE-ID, and then pull the historical archived data and real-time stream data of the module from the corresponding data source. This can ensure a high degree of matching between the analysis object and the specific unwanted event and avoid interference from irrelevant data.

[0134] For example, for the Automatic Emergency Braking System (AEBS) of intelligent connected vehicles, when a potential risk prediction request for an undesirable event of radar mis-triggered braking is received, the MSR module of the radar sensor can be associated with the UE-ID to extract the historical radar signal-to-noise ratio curve of the past 6 months (MSR historical monitoring data) and the real-time echo intensity data during the current driving process (MSR real-time monitoring data).

[0135] Furthermore, in the process of predicting the potential risks of failure modes corresponding to undesirable events, this application can utilize machine learning algorithms or statistical models, using extracted historical MSR monitoring data as a training set or benchmark, and real-time MSR monitoring data as input features, to calculate the probability and consequences of a specific failure mode occurring within a specific time window in the future, i.e., the potential risks of failure modes corresponding to undesirable events. This enables a shift from post-event review to pre-event warning, providing a direct basis for subsequent preventive maintenance decisions and adjustments to control strategies, effectively reducing the incidence of sudden failures, and ensuring the reliability of the product throughout its entire lifecycle.

[0136] Potential risks include failure probability, failure impact scope, and time-series trends. Failure probability can be understood as the mathematical likelihood of a particular failure mode occurring within a predetermined future timeframe, usually expressed as a percentage. Failure impact scope can be understood as the number of product functional modules that may be affected, the types of user scenarios that will be impacted, or the level of economic loss that may result after an undesirable event and its corresponding failure mode occur. Time-series trends can be understood as the dynamic trajectory of failure risk over time, such as an increase, decrease, or periodic fluctuation in risk level.

[0137] As an example, this application can use time series analysis models (such as ARIMA, LSTM neural networks) to fit historical data, identify the evolution patterns of key parameters that lead to failure, and then substitute real-time data into the model for extrapolation.

[0138] As another example, based on historical data and real-time monitoring values ​​of voltage fluctuations, the model predicts that in the next two weeks, the probability of a certain power control module failing due to communication interruption caused by voltage instability will increase from the current 5% to 35%, and its impact will expand to the entire transmission system, showing an accelerating deterioration trend.

[0139] In some embodiments, such as Figure 2 As shown, the analysis method for FMEA data includes steps S110, S120, S130, and S140.

[0140] S110. Based on the product's unexpected events, correlate the product's FMEA data to obtain correlation information; S120. In response to the product's risk analysis request, based on the relevant information, obtain the data to be analyzed from the FMEA data, perform risk analysis on the data to be analyzed, and obtain the product's risk analysis results. S130. In response to a visualization request for the risk analysis results, generate visualization analysis results and early warning information for the risk analysis results based on the request type of the visualization request. S140. Based on undesirable events, the visualization analysis results and early warning information are displayed in a hierarchical and dynamic risk visualization, and the FMEA data is optimized.

[0141] In this embodiment, a visualization request can be understood as an instruction issued by the user terminal to the analysis system to request the risk analysis results to be displayed in a graphical or structured form.

[0142] The request type can be determined based on the user role making the request or the risk dimensions they are concerned with, such as global overview, local penetration, or detailed drill-down.

[0143] Visualization analysis results can be generated based on risk analysis results (such as failure chain tracing reports, effectiveness analysis information, correlation impact, or potential risk prediction data) and combined with pre-set visualization templates to create graphical data. The forms of visualization analysis results include, but are not limited to, topology diagrams, process flow diagrams, trend graphs, or data dashboards.

[0144] Early warning information can be understood as alert data generated when a specific indicator in the risk analysis results (such as Risk Priority Number, Probability of Failure, or Time-Series Trend) exceeds a preset threshold. Early warning information may include the warning level (e.g., red, orange, and blue), the ID of the unwanted event that triggered the warning, the associated failure mode, and recommended remedial measures.

[0145] Layered dynamic risk visualization can be understood as using undesirable events as the core index, organizing the visualization analysis results and early warning information according to the logical hierarchy of global, local, and detailed, and realizing real-time synchronous updates and interactive linkage of data on the display interface.

[0146] Specifically, in the process of generating visualized analysis results and early warning information of risk analysis results and displaying them in a hierarchical dynamic risk visualization manner, this application can first parse the request type parameter in the visualization request. If the request type is global overview, which usually corresponds to decision-making level users, then the risk level data of all undesirable events can be aggregated to generate a topology map reflecting the risk distribution of the entire product. At the same time, nodes of different colors in the topology map can represent undesirable events of different risk levels. If the request type is local penetration, which corresponds to management level users, then the full-process correlation data of the specified undesirable event can be extracted to generate a flowchart connecting elements, functions, failure chains, control, and execution. If the request type is detailed drill-down, which corresponds to execution level users, then the full-dimensional detailed data corresponding to a single undesirable event can be retrieved to generate a combined chart containing detailed parameters, historical curves, and monitoring records.

[0147] Meanwhile, when generating the visualization analysis results and early warning information of the risk analysis results, the key indicators in the risk analysis results can be monitored in real time. When the failure probability of an unwanted event is detected to be rising and exceeds the orange warning threshold, an orange warning information containing the event ID and the scope of impact is immediately generated and marked in the corresponding visualization analysis results.

[0148] This application has a mechanism for dynamically generating visual results and early warning information based on the request type, which can adapt to the decision-making needs of users at different levels. This can transform abstract FMEA data analysis results into an intuitive risk view, and through an instant early warning mechanism, it can ensure that high-risk hazards can be captured in a timely manner.

[0149] Specifically, in the process of optimizing FMEA data, this application can, during the visualization process, update the original records, relationships, or risk parameters in the FMEA database in reverse based on user interaction on the interface (such as marking missing data and correcting correlations) or data anomalies automatically identified by the system (such as excessive deviation between monitoring data and FMEA predictions), forming a closed loop of analysis, display, and optimization. This enables the intuitive presentation and cross-level penetration of risk information, while continuously correcting and improving the basic FMEA data by utilizing user feedback and real-time data differences during the visualization process, significantly improving the accuracy and timeliness of FMEA analysis.

[0150] In terms of layered display, the global layer shows the risk distribution of all unforeseen events in the form of a topology diagram. Users can click on high-risk nodes (such as the UE-ID node displayed in red) to drill down to the local layer. The local layer unfolds the complete link of the elements, functions, failure chains and control measures corresponding to the unforeseen event in the form of a flowchart. If there is a warning message, it will be highlighted and flashed on the corresponding link node. The detail layer further shows the parameter details of the specific failure mode in the link, the real-time waveform of MSR monitoring and the design document link.

[0151] In terms of dynamism, when the underlying MSR monitoring module collects new abnormal data or the design document changes, it automatically refreshes the visualization content at each level through the event ID, without the need for manual reloading.

[0152] Regarding data optimization, if an execution-level user discovers that the record of a failure cause does not match the actual failure phenomenon at the detail level, they can directly initiate a data correction request through the interface. After confirmation, the description of the failure cause and its association weight with the undesirable event in the FMEA database can be automatically updated. Alternatively, when the visualization results show that the frequency of a certain type of undesirable event is much higher than the initial FMEA assessment value, the risk parameter recalculation process is automatically triggered, updating the RPN value and risk level corresponding to the undesirable event, and synchronizing the optimized data back to the FMEA requirement database.

[0153] In this application, targeted analysis results and early warning information are generated by responding to visualization requests, and a hierarchical dynamic display system is constructed based on unexpected events, realizing comprehensive risk visualization from macro-level decision-making to micro-level execution.

[0154] Meanwhile, by deeply integrating the visualization and FMEA data optimization process, and utilizing data deviations or user correction instructions discovered during the visualization process, the FMEA database is driven to iterate and update. This effectively solves the problems of static and rigid data and the disconnect between visualization and physical products in product FMEA analysis, ensuring the dynamic adaptability and self-evolution capability of the risk management system. As a result, the efficiency of risk decision-making and the accuracy of control throughout the entire life cycle of complex products are greatly improved.

[0155] In some embodiments, the FMEA data analysis method further includes: in response to optimization information from the FMEA data, optimizing risk analysis results, visualization analysis results, and early warning information based on undesirable events.

[0156] In this embodiment, optimization information can be understood as data change instructions or updates generated during the monitoring or review of the entire product lifecycle. This optimization information may include drawing changes during the design phase, process adjustments during the production phase, new fault cases during the operation and maintenance phase, and real-time anomaly records from the MSR module.

[0157] Specifically, when optimization information is received, the identifier field carried in the information can be parsed first to extract the corresponding unwanted event label (UE-ID).

[0158] Furthermore, in the process of optimizing risk analysis results, visualization analysis results, and early warning information based on undesirable events, bidirectional penetration can be performed in the constructed six-layer association model based on UE-ID to locate the element ID, function label (FUNC-ID), failure chain label (FC / FM / FE-ID), and control requirement ID directly associated with the undesirable event, thereby triggering recalculation and status refresh for these associated nodes.

[0159] For example, when the voltage tolerance parameters of the power module of an intelligent connected vehicle are updated during the design iteration, after receiving FMEA data optimization information containing the new parameter values, it can immediately identify the undesirable event to which the change belongs as power output interruption, and automatically lock all associated failure modes and risk parameters under the event.

[0160] Furthermore, in the process of optimizing the risk analysis results, this application can use the updated FMEA data to re-execute the multi-dimensional fusion analysis logic and generate a revised risk assessment conclusion.

[0161] Specifically, this application can recalculate the Risk Priority Number (RPN), failure probability, and impact scope based on the latest design parameters or historical failure data, and assess whether the effectiveness of existing control measures has changed. If the optimization information indicates that a certain control requirement has been implemented and effective, the risk level in the risk analysis results should be lowered accordingly; conversely, if a new understanding of failure under a certain extreme operating condition is added, the risk level needs to be raised and potential interlocking risks supplemented.

[0162] Furthermore, in the process of optimizing the visualization analysis results, this application can map the refreshed risk analysis data onto a hierarchical dynamic risk visualization interface, and update the content of the global topology diagram, local link flowchart, and detailed data display layer in real time. Specifically, it can adjust the color coding of nodes to reflect the new risk level, update the curve trend in the trend diagram to show the latest RPN change trajectory, and reconstruct the annotation information of each link in the associated link diagram.

[0163] Furthermore, in the process of optimizing early warning information, this application can dynamically adjust the triggering status, level classification, and push content of early warning signals based on the updated risk threshold and prediction results. If the optimized data shows that the risk has dropped to a safe range, the original red or orange warning will be automatically lifted; if a new potential high-risk trend is found, a new early warning pop-up will be generated immediately and pushed to the relevant responsible persons.

[0164] This application achieves dynamic self-adaptation of FMEA data closed loop through synchronous optimization of risk analysis results, visualization presentation and early warning mechanism, ensuring the timeliness and authority of decision support information, effectively avoiding misjudgment and omission due to data lag, thereby significantly improving the accuracy and response speed of risk management of complex products throughout their entire life cycle.

[0165] In the FMEA data analysis method provided in this invention embodiment, scattered FMEA data are uniformly correlated through undesirable events of the product, constructing a structured correlation information with undesirable events as the core. This enables the correlation of isolated elements, element functions, failure chains, control requirements, design documents, production documents, and MSR modules from multiple sources. Consequently, when responding to risk analysis requests, the data to be analyzed can be accurately extracted through the correlation information and in-depth analysis can be performed. This effectively solves the problems of incomplete risk views, difficulty in root cause location, and decision lag caused by data fragmentation and missing correlations in FMEA data analysis. It realizes the transformation from single-parameter statistics to multi-dimensional integrated analysis, significantly improving the accuracy of product risk identification, the completeness of traceability, and the pertinence of control measures. Thus, it can provide reliable data support and decision-making basis for the full life cycle safety management of complex products.

[0166] In some embodiments, the present invention also provides an FMEA data analysis apparatus 200, which is used to perform any of the aforementioned FMEA data analysis methods.

[0167] Specifically, please refer to Figure 3 , Figure 3 This is a schematic block diagram of the FMEA data analysis device 200 provided in an embodiment of the present invention.

[0168] like Figure 3 As shown, the FMEA data analysis apparatus 200 provided in this application includes: an association unit 210 and a response analysis unit 220.

[0169] The association unit 210 is used to associate the product's FMEA data based on the product's undesirable events to obtain association information; the response analysis unit 220 is used to respond to the product's risk analysis request, obtain the data to be analyzed from the FMEA data according to the association information, and perform risk analysis on the data to be analyzed to obtain the product's risk analysis results.

[0170] The FMEA data analysis device 200 provided in this application embodiment can unify and correlate scattered FMEA data through unexpected events of the product, constructing a structured correlation information with unexpected events as the core. This enables the correlation of multi-source data such as isolated elements, element functions, failure chains, control requirements, design documents, production documents, and MSR modules. In response to risk analysis requests, it can accurately extract the data to be analyzed and perform in-depth analysis through correlation information. This effectively solves the problems of incomplete risk views, difficulty in root cause location, and decision lag caused by data fragmentation and missing correlations in FMEA data analysis. It realizes the transformation from single-parameter statistics to multi-dimensional integrated analysis, significantly improving the accuracy of product risk identification, the completeness of traceability, and the pertinence of control measures. In this way, it can provide reliable data support and decision-making basis for the full life cycle safety management of complex products.

[0171] It should be noted that those skilled in the art can clearly understand that the specific implementation process of the above-mentioned FMEA data analysis device and each unit can be referred to the corresponding description in the foregoing method embodiments. For the sake of convenience and brevity, it will not be repeated here.

[0172] The aforementioned FMEA data analysis device can be implemented as a computer program, which can perform operations such as... Figure 4 It runs on the electronic device shown.

[0173] Please see Figure 4 , Figure 4 This is a schematic block diagram of an electronic device provided in an embodiment of the present invention.

[0174] See Figure 4 The device 300 includes a processor 302, a memory, and a network interface 305 connected via a system bus 301. The memory may include a storage medium 303 and internal memory 304.

[0175] The storage medium 303 may store an operating system 3031 and a computer program 3032. When the computer program 3032 is executed, it enables the processor 302 to perform FMEA data analysis methods.

[0176] The processor 302 provides computing and control capabilities to support the operation of the entire device 300.

[0177] The internal memory 304 provides an environment for the execution of the computer program 3032 in the non-volatile storage medium 303. When the computer program 3032 is executed by the processor 302, the processor 302 can execute the FMEA data analysis method.

[0178] This network interface 305 is used for network communication, such as providing the transmission of definition information. Those skilled in the art will understand that... Figure 4 The structure shown is merely a block diagram of a portion of the structure related to the present invention and does not constitute a limitation on the device 300 to which the present invention is applied. The specific device 300 may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0179] The processor 302 is used to run the computer program 3032 stored in the memory to perform the following functions: based on the product's undesirable events, it correlates the product's FMEA data to obtain correlation information; in response to the product's risk analysis request, it retrieves the data to be analyzed from the FMEA data according to the correlation information, performs risk analysis on the data to be analyzed, and obtains the product's risk analysis results.

[0180] Those skilled in the art will understand that Figure 4 The embodiments of device 300 shown do not constitute a limitation on the specific configuration of device 300. In other embodiments, device 300 may include more or fewer components than shown, or combine certain components, or have different component arrangements. For example, in some embodiments, device 300 may include only a memory and processor 302. In such embodiments, the structure and function of the memory and processor 302 are similar to those shown. Figure 4 The embodiments shown are consistent and will not be described again here.

[0181] It should be understood that, in this embodiment of the invention, the processor 302 may be a central processing unit (CPU), or it may be other general-purpose processors 302, digital signal processors 302 (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor 302 may be a microprocessor 302, or it may be any conventional processor 302, etc.

[0182] According to one aspect of this application, a computer program product or computer program is also provided, comprising computer instructions stored in a computer-readable storage medium. A processor of an electronic device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the electronic device to perform the following steps: correlating FMEA data of the product based on undesirable events of the product to obtain correlation information; and, in response to a risk analysis request for the product, obtaining data to be analyzed from the FMEA data according to the correlation information and performing risk analysis on the data to be analyzed to obtain a risk analysis result for the product.

[0183] It will be understood by those skilled in the art that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program includes program instructions and can be stored in a storage medium, which is a computer-readable storage medium. The program instructions are executed by at least one processor in the computer system to implement the process steps of the embodiments of the above methods.

[0184] In another embodiment of the invention, a computer storage medium is provided. This storage medium may be a non-volatile computer-readable storage medium or a volatile storage medium. The storage medium stores a computer program 3032, which, when executed by a processor 302, performs the following steps: correlating the product's FMEA data based on undesirable events to obtain correlation information; and, in response to a risk analysis request for the product, retrieving data to be analyzed from the FMEA data according to the correlation information, performing risk analysis on the data to be analyzed, and obtaining the product's risk analysis results.

[0185] The storage medium can be any computer-readable storage medium that can store program code, such as a USB flash drive, external hard drive, read-only memory (ROM), magnetic disk, or optical disk.

[0186] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this application.

[0187] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative. For example, the division of each unit is merely a logical functional division, and there may be other division methods in actual implementation. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed.

[0188] The steps in the methods of this application embodiment can be adjusted, merged, or deleted according to actual needs. The units in the apparatus of this application embodiment can be merged, divided, or deleted according to actual needs. Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0189] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause an electronic device (which may be a personal computer, a terminal, or a network device, etc.) to execute all or part of the steps of the methods provided in the various embodiments of this application.

[0190] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and these modifications or substitutions should all be covered within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for analyzing FMEA data, characterized in that, include: Based on the unexpected events of the product, the FMEA data of the product is correlated to obtain correlation information; In response to the risk analysis request for the product, the data to be analyzed is obtained from the FMEA data based on the associated information, and the risk analysis is performed on the data to be analyzed to obtain the risk analysis results for the product.

2. The method for analyzing FMEA data according to claim 1, characterized in that, The undesirable events based on the product are used to correlate the product's FMEA data to obtain correlation information, including: Obtain the FMEA data; the FMEA data includes the product's elements, element functions, failure chain, undesirable events, control requirements, design documents, production documents, and MSR module; The association information is obtained by associating at least one of the elements, the function of the elements, the failure chain, the control requirements, the design document, the production document, and the MSR module with the unwanted event.

3. The method for analyzing FMEA data according to claim 2, characterized in that, The step of associating at least one of the elements, element functions, failure chains, control requirements, design documents, production documents, and the MSR module with the undesirable event to obtain the association information includes: Extract the first tag of the element, the second tag of the element function, the third tag of the failure chain, the fourth tag of the undesirable event, the fifth tag of the control requirement, the first document tag of the design document, the second document tag of the production document, and the sixth tag of the MSR module from the FMEA data; The first tag, the second tag, the third tag, the fifth tag, the first document tag, the second document tag, and the sixth tag are associated with the fourth tag to obtain the association information.

4. The method for analyzing FMEA data according to claim 3, characterized in that, The step of associating the first tag, the second tag, the third tag, the fifth tag, the first document tag, the second document tag, and the sixth tag with the fourth tag to obtain the association information includes: Associating the fourth tag with the first tag yields the first sub-association information; Based on the first sub-association information, the first tag is associated with the second tag to obtain the second sub-association information; Based on the second sub-association information, the second tag is associated with the third tag to obtain the third sub-association information; Based on the third sub-association information, the third tag is associated with the fifth tag, the first document tag, the second document tag, and the sixth tag to obtain the association information.

5. The method for analyzing FMEA data according to claim 1, characterized in that, The step of obtaining the data to be analyzed from the FMEA data based on the associated information, and performing risk analysis on the data to be analyzed to obtain the risk analysis results of the product includes: If the risk analysis request is a failure chain tracing analysis request for the product, extract the element information, element function information, failure chain information, unexpected event information, historical fault data and MSR abnormal records corresponding to the undesirable event from the FMEA data according to the association information. Based on the historical fault data and the MSR anomaly records, the target failure mode, target failure cause, and interlocking risk of the undesirable event are extracted from the element information, the element function information, the failure chain information, and the undesirable event information. Based on the undesirable event, the target failure mode, the target failure cause, and the interlocking risk, a failure chain tracing report for the product is generated.

6. The method for analyzing FMEA data according to claim 1, characterized in that, The risk analysis results also include information on the effectiveness of controlling the product's functional elements and failure chains; The step of obtaining the data to be analyzed from the FMEA data based on the associated information, and performing risk analysis on the data to be analyzed to obtain the risk analysis results of the product includes: If the risk analysis request is a dynamic analysis request for the risk parameters of the product, the RPN parameters, control information and MSR response information corresponding to the undesirable event are extracted from the FMEA data according to the associated information; Based on the RPN parameters, the control information, and the MSR response information, effectiveness analysis information for controlling the functional elements and failure chains of the product is generated.

7. The method for analyzing FMEA data according to claim 1, characterized in that, The risk analysis results also include the correlation and impact between various stages in the product FMEA analysis; The step of obtaining the data to be analyzed from the FMEA data based on the associated information, and performing risk analysis on the data to be analyzed to obtain the risk analysis results of the product includes: If the risk analysis request is a full-process correlation analysis request of the product FMEA, the failure mode information, control requirement information, design and production information and monitoring response information corresponding to the undesirable event are extracted from the FMEA data according to the correlation information. Based on the failure mode information, the control requirements information, the design and production information, and the monitoring response information, the correlation and impact between each link in the product FMEA analysis are generated.

8. The method for analyzing FMEA data according to claim 1, characterized in that, The risk analysis results also include the potential risks of the product; The step of obtaining the data to be analyzed from the FMEA data based on the associated information, and performing risk analysis on the data to be analyzed to obtain the risk analysis results of the product includes: If the risk analysis request is a potential risk prediction analysis request for the product, extract the MSR historical monitoring data and MSR real-time monitoring data corresponding to the undesirable event from the FMEA data based on the associated information; Based on the historical monitoring data and real-time monitoring data of the MSR, the potential risks of the failure modes corresponding to the undesirable events are predicted; the potential risks include failure probability, failure impact range, and timing trend.

9. The method for analyzing FMEA data according to any one of claims 1-8, characterized in that, After performing risk analysis on the data to be analyzed and obtaining the risk analysis results for the product, the method further includes: In response to a visualization request for the risk analysis results, based on the request type of the visualization request, a visualization analysis result and a warning message for the risk analysis results are generated; Based on the undesirable events, the visualization analysis results and the early warning information are displayed in a hierarchical dynamic risk visualization, and the FMEA data is optimized.

10. An electronic device, characterized in that, The device includes a memory and a processor, the memory storing a computer program, characterized in that the processor executes the computer program to implement the steps of the FMEA data analysis method according to any one of claims 1 to 9.

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