A modular component quick-change electrical automation distribution cabinet maintenance system

Abstract

This invention relates to the field of electrical automation technology and discloses a maintenance system for electrical automation distribution cabinets with modular component rapid replacement. The system's status data acquisition module collects multi-dimensional operational status data of each modular component within the distribution cabinet in real time, generating an equipment status data warehouse after time alignment and standardization. A topology mapping module reduces the dimensionality of the data warehouse, extracts component status feature vectors, constructs a local topology map based on the physical connection relationships of the components, and merges them into a global topology map. An anomaly marking module calculates the dynamic deviation of the component status feature vectors and marks potential abnormal component sequences based on the correlation strength between adjacent nodes in the global topology map. A maintenance decision module generates component replacement priorities and operation instruction sets based on the potential abnormal component sequences and a preset maintenance strategy library, which can improve the comprehensiveness, accuracy, and efficiency of distribution cabinet maintenance and ensure the stable operation of the electrical automation system.

Description

Technical Field

[0001] This invention relates to the field of electrical automation technology, specifically to an electrical automation distribution cabinet maintenance system with modular components that allow for rapid replacement. Background Technology

[0002] In the field of electrical automation, the switchboard serves as the core carrier for power distribution and equipment control, and its stable operation directly affects the reliability of the entire automation system. With the continuous improvement of industrial automation, the number of components integrated within the switchboard is constantly increasing, and it is gradually developing towards modularity. Modular components with different functions achieve rapid assembly and functional expansion through standardized interfaces. While this trend enhances the flexibility and adaptability of the switchboard, it also places higher demands on maintenance.

[0003] Currently, there are several common problems in the maintenance of power distribution cabinets. First, in the status data acquisition stage, traditional methods mostly monitor single components or a few key parameters, lacking comprehensive coverage of all modular components in the power distribution cabinet. Moreover, the data formats and acquisition frequencies of different components differ, making it difficult to effectively integrate the data and form a complete view of the equipment's operating status. As a result, it is difficult for staff to accurately grasp the overall operating situation.

[0004] In component correlation analysis, traditional maintenance methods often neglect the physical connections and functional relationships between modular components, treating each component as an independent entity for analysis. When a component shows abnormal signs, it is impossible to trace its correlation with adjacent or other related components in a timely manner, making it difficult to accurately determine the root cause of the abnormality and the potential scope of its spread. This can easily lead to one-sided maintenance decisions and increase the difficulty and time cost of troubleshooting.

[0005] In the anomaly identification and maintenance decision-making process, traditional methods rely heavily on the experience of staff, lacking scientific quantitative analysis tools. After discovering problems through regular inspections or equipment alarms, staff often need to check related components one by one, which is not only inefficient but also makes it difficult to effectively identify potential anomalies that have not yet shown obvious fault characteristics, easily leading to overlooked potential problems. Furthermore, the lack of a unified standard for determining component replacement priorities, based on the actual operating status of the equipment and system requirements, may result in untimely replacement of critical components or over-maintenance of non-critical components, affecting the operational stability and maintenance economy of the distribution cabinet.

[0006] With the increasing demands for reliability of electrical automation systems in industrial production, traditional distribution cabinet maintenance methods are no longer sufficient to meet actual needs. There is an urgent need for a maintenance system that can achieve comprehensive monitoring, accurate analysis, and efficient decision-making to improve the scientific nature and timeliness of distribution cabinet maintenance and ensure the stable operation of electrical automation systems. Summary of the Invention

[0007] The purpose of this invention is to provide an electrical automation distribution cabinet maintenance system with modular components that allows for rapid replacement, in order to solve the problems mentioned in the background art.

[0008] To achieve the above objectives, the present invention provides a modular component quick-replacement electrical automation distribution cabinet maintenance system, the system comprising:

[0009] The status data acquisition module is used to collect multi-dimensional operating status data of each modular component in the power distribution cabinet in real time, perform time alignment and standardization processing on the multi-dimensional operating status data, and generate a device status data warehouse.

[0010] The topology mapping module is used to perform feature dimensionality reduction processing on the equipment status data warehouse, extract the status feature vectors of each modular component, construct a local topology map based on the physical connection relationship between adjacent components, and merge all local topology maps to generate a global topology map of the power distribution cabinet.

[0011] An anomaly marking module is used to calculate the dynamic deviation of the state feature vector of each modular component, and mark the sequence of potential abnormal components by combining the correlation strength of adjacent nodes in the global topology map of the distribution cabinet.

[0012] The maintenance decision module is used to generate replacement priorities and operation instruction sets for modular components based on the potential abnormal component sequence and a preset maintenance strategy library.

[0013] Preferably, the multi-dimensional operating status data includes electrical parameter data, mechanical vibration data, temperature distribution data, ambient humidity data, and component lifespan data.

[0014] Preferably, the time alignment and standardization processing of the multi-dimensional operational status data specifically includes:

[0015] Electrical parameter data, mechanical vibration data, temperature distribution data, ambient humidity data, and component life data with different sampling frequencies are interpolated and aligned according to a unified timestamp.

[0016] Normalization calculations were performed on the data for each dimension to eliminate differences in units;

[0017] The integrated and normalized multi-dimensional data forms a device status data warehouse.

[0018] Preferably, extracting the state feature vectors of each modular component specifically includes:

[0019] Separate the independent operating data of each modular component from the device status data warehouse;

[0020] Principal component analysis was performed on the independent operating data of each modular component to extract core state indicators;

[0021] The core status indicators are combined into a status feature vector according to preset dimensions.

[0022] Preferably, constructing a local topology graph specifically includes:

[0023] Identify the modular component groups that are directly physically connected in the global topology map of the power distribution cabinet;

[0024] Calculate the similarity measure of the state feature vectors of each component within the same group;

[0025] Generate connection weights between components based on similarity metrics;

[0026] Local topology graphs are drawn based on connection weights.

[0027] Preferably, the sequence of potentially anomalous components is specifically included in:

[0028] Calculate the deviation between the current state feature vector of each modular component and the historical baseline vector;

[0029] Locate component nodes whose deviation exceeds a threshold in the global topology map of the power distribution cabinet;

[0030] Traverse the neighboring nodes of this node, and mark it as an abnormal component if the association strength is greater than a preset value;

[0031] Integrate all marked components to generate a sequence of potential anomalous components.

[0032] Preferably, the generation of replacement priorities specifically includes:

[0033] Obtain the deviation of each component and the number of associated components in the potential abnormal component sequence;

[0034] The component failure risk coefficient is calculated by multiplying the deviation by the number of associated components.

[0035] A replacement priority list is generated by sorting the replacements in descending order of failure risk coefficient.

[0036] Preferably, the generated operation instruction set specifically includes:

[0037] Analyze the position coordinates of each component in the replacement priority list in the global topology map of the power distribution cabinet;

[0038] Match the disassembly path template of the corresponding component type in the maintenance strategy library;

[0039] Optimize the tool operation sequence in the disassembly path template by combining location coordinates;

[0040] The output includes a set of instructions containing the tool type, operation steps, and safety parameters.

[0041] Preferably, the optimized disassembly path template includes:

[0042] Detect the location coordinates of multiple modular components involved in the current maintenance task;

[0043] Calculate the shortest physical path between each coordinate;

[0044] The execution order of the disassembly steps is reorganized according to the shortest physical path;

[0045] Add a check node to the disassembly path template to prevent accidental operation.

[0046] Preferably, the maintenance decision module is further used for:

[0047] The device status data warehouse is updated in real time by receiving newly collected multi-dimensional operational status data.

[0048] The topology mapping module is triggered based on the updated equipment status data warehouse to regenerate the global topology map of the power distribution cabinet;

[0049] The instruction set for iterative updating and maintenance of the global topology map of the new power distribution cabinet.

[0050] Compared with the prior art, the beneficial effects of the present invention are:

[0051] This modular, quick-change electrical automation distribution cabinet maintenance system offers several significant advantages for distribution cabinet maintenance through the collaborative work of its modules. In terms of status data acquisition, the system can collect multi-dimensional operational status data from each modular component within the distribution cabinet in real time, moving beyond limitations to a single component or a few parameters to achieve comprehensive coverage of the operational status of all components. Simultaneously, by performing time alignment and standardization processing on the collected multi-dimensional operational status data, the system effectively solves the problem of inconsistent data formats and acquisition frequencies among different components, forming a unified and standardized equipment status data warehouse. Staff can obtain complete and accurate equipment operational status information from this warehouse, clearly understanding the real-time operating status of each component, providing a comprehensive data foundation for subsequent analysis and decision-making.

[0052] At the component correlation analysis level, the topology mapping module performs feature dimensionality reduction on the equipment status data warehouse, extracting the status feature vectors of each modular component. This removes redundant information while retaining key features, improving data processing efficiency. More importantly, this module constructs local topology maps based on the physical connections between adjacent components and merges all local topology maps to generate a global topology map of the distribution cabinet, clearly presenting the physical connections and functional relationships between each modular component. This global topology map construction allows staff to intuitively understand the location and mutual influence of each component within the entire distribution cabinet system. When a component malfunctions, its association with other components can be quickly traced, accurately determining the potential chain reactions caused by the malfunction. This avoids the one-sided analysis caused by neglecting component correlations in traditional maintenance, providing strong support for accurately locating the root cause of the malfunction.

[0053] In the anomaly identification phase, the anomaly marking module calculates the dynamic deviation of the state feature vectors of each modular component, enabling quantitative analysis of the difference between the component's operating state and its normal state. This overcomes the limitations of traditional experience-based judgment, making anomaly identification more scientific and objective. Simultaneously, by analyzing the correlation strength of adjacent nodes in the global topology map of the distribution cabinet, it can not only identify components exhibiting obvious anomaly characteristics but also discover potential anomalies that have not yet shown serious signs of failure, marking the sequence of potential anomalies. This effectively avoids overlooking potential faults, provides early warnings of possible failures, and buys time for timely maintenance measures.

[0054] In terms of maintenance decision-making, the maintenance decision-making module generates replacement priorities and operation instruction sets based on potential abnormal component sequences and a pre-set maintenance strategy library. This ensures that the determination of component replacement priorities no longer relies on subjective experience, but rather on the actual abnormality of the component, its importance in the system, and the pre-set maintenance strategy, making the judgment of replacement priorities more reasonable and fair. Simultaneously, the generated operation instruction sets provide clear and specific operational guidance for staff, avoiding operational confusion or errors during maintenance and significantly improving the standardization and efficiency of maintenance operations. Staff can carry out component replacement work in an orderly manner according to the replacement priority, prioritizing abnormal components that have a greater impact on system operation, reducing the interference of faults on the operation of distribution cabinets and the entire electrical automation system, while rationally scheduling the maintenance of non-critical components to avoid resource waste caused by over-maintenance. This ensures stable system operation while improving the economy and overall efficiency of maintenance work. Attached Figure Description

[0055] Figure 1 This is a timing diagram of the electrical automation distribution cabinet maintenance system with modular component quick replacement as described in this invention;

[0056] Figure 2A flowchart for time alignment and standardization processing of multi-dimensional operational status data;

[0057] Figure 3 A flowchart for constructing a local topology graph;

[0058] Figure 4 A flowchart for labeling potential anomalous component sequences. Detailed Implementation

[0059] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0060] Please see Figure 1 This invention provides a modular component quick-replacement electrical automation distribution cabinet maintenance system. The system comprises a status data acquisition module, a topology mapping module, an anomaly marking module, and a maintenance decision module. The specific implementation is as follows.

[0061] The status data acquisition module is responsible for collecting multi-dimensional operational status data of each modular component within the distribution cabinet in real time, and performing time alignment and standardization on this data to generate an equipment status data warehouse. The topology mapping module performs feature dimensionality reduction on the equipment status data warehouse, extracts the status feature vectors of each modular component, constructs a local topology map based on the physical connections between adjacent components, and merges all local topology maps to generate a global topology map of the distribution cabinet. The anomaly marking module calculates the dynamic deviation of the status feature vectors of each modular component, and, combined with the correlation strength of adjacent nodes in the global topology map of the distribution cabinet, marks potential anomaly component sequences. The maintenance decision module generates replacement priorities and operation instruction sets for modular components based on the potential anomaly component sequences and a pre-set maintenance strategy library. This system, by integrating data acquisition, processing, analysis, and decision-making functions, achieves automated and intelligent maintenance of the distribution cabinet.

[0062] Example 1: See Figure 2The execution process of the status data acquisition module is demonstrated through an actual power distribution cabinet operation scenario. This cabinet is installed in a power distribution room in an industrial environment and contains multiple modular components, such as intelligent circuit breakers, contactors, frequency converters, power modules, and various protective relays. These components are electrically connected via busbars and cables of various specifications, forming a complete power supply network. The acquisition of multi-dimensional operational status data is fundamental to system operation. Electrical parameter data is acquired through sensor groups installed on the main and branch circuits. Voltage and current are measured non-contactly using Hall effect sensors, while the power factor is calculated using a dedicated metering chip. These electrical parameters are recorded at a high sampling frequency, for example, current and voltage data are collected 1000 times per second to capture possible instantaneous fluctuations and harmonics. Mechanical vibration data is acquired through miniature accelerometers, which are directly attached to the surfaces of components prone to mechanical movement, such as circuit breaker operating mechanisms and contactor electromagnetic systems. Vibration data is recorded at a slightly lower frequency, such as 100 times per second, to monitor mechanical wear, loosening, or jamming of components. Temperature distribution data acquisition relies on two types of sensors: surface-mount PT100 temperature sensors are installed at high-current connection points, power device heat sinks, and other critical heat-generating components; non-contact infrared temperature sensors are strategically placed inside the cabinet, periodically scanning large areas to generate temperature field distribution maps. Ambient humidity data is collected by digital humidity sensors located at the top and bottom of the cabinet to monitor condensation risk. Component lifespan data is not directly measured but calculated by the system based on two main factors: first, the cumulative power-on operating time of each component, recorded by the controller's internal clock; and second, an estimate based on the known lifespan curve of the component model and the actual operating conditions it has experienced.

[0063] These heterogeneous data streams have different acquisition frequencies and timings. Electrical parameters change the fastest and have the highest sampling frequency; mechanical vibration is next; temperature and environmental data change relatively slowly and have a lower sampling frequency; lifespan data are cumulative values ​​measured in hours or days. The primary task of the status data acquisition module is to perform time alignment and standardization on these data. The system uses a high-precision global clock source to timestamp all data uniformly. For data with inconsistent sampling times, the system uses a linear interpolation algorithm for calculation. For example, if vibration data is needed at a certain millisecond, but the nearest vibration sampling point may exist a few milliseconds ago or later, the system calculates the vibration value at that precise moment based on the values ​​of these two nearest points. Similarly, for parameters that change slowly, such as temperature, similar interpolation alignment is performed to ensure that all data have valid values ​​on a unified time axis.

[0064] After time alignment, data for each dimension are normalized. For electrical parameters, voltage values ​​may be hundreds of volts, and current values ​​may be tens to hundreds of amperes. These data are converted to the range [0,1], based on the upper and lower limits of the parameter's normal operating range. For example, if the rated current of a circuit is 100A, the measured value divided by 100 is the normalized result. Mechanical vibration data is normalized according to the sensor range and normal vibration amplitude. Temperature data is normalized according to the component's maximum allowable operating temperature. Humidity data is directly expressed as a percentage, divided by 100. Lifespan data is expressed as the ratio of current consumed lifespan to estimated total lifespan. This series of processing eliminates numerical differences caused by different physical dimensions, allowing data of different properties to be comprehensively analyzed and compared.

[0065] All this time-aligned and normalized multi-dimensional data is integrated and stored to form a device status data warehouse. This warehouse is organized using a time-series database structure, with each record containing a precise timestamp, a unique component identifier, and a set of normalized multi-dimensional data values. This structured storage method not only comprehensively records the operational status of each component over time but also provides a solid data foundation for efficient data retrieval and in-depth analysis by subsequent modules. The entire data acquisition and processing workflow operates automatically without manual intervention, continuously providing standardized, high-quality data input for system status monitoring and maintenance decisions.

[0066] Example 2: See Figure 3 The function of the topology mapping module is illustrated by handling an example of a distribution cabinet containing twelve main modular components. These components include incoming circuit breakers, capacitor compensation modules, multiple outgoing circuit breakers, contactor groups, and control power modules. These components are interconnected by copper busbars and cables to form a complete power distribution system.

[0067] The equipment status data warehouse stores standardized, multi-dimensional operational data for these components. When extracting the status feature vectors of each modular component, the system first separates the independent operational data of each component from the warehouse based on its unique component identifier. Taking the No. 3 outgoing circuit breaker as an example, its independent data includes electrical parameters (current, voltage, power factor) over a continuous time series, mechanical vibration data, temperature readings from multiple points, ambient humidity data, and life indicators based on operating time. This data constitutes a high-dimensional status description matrix.

[0068] Principal component analysis was performed on the high-dimensional data of each component. This analysis aims to find the direction with the largest variance contribution in the data, transforming multiple potentially related variables into a few independent comprehensive indicators. For circuit breaker No. 3, its twelve original operating parameters were reduced to three core state indicators through transformation: the first indicator comprehensively reflects the coupling state of electrical load and thermal effects; the second indicator captures the characteristics of mechanical vibration modes; and the third indicator characterizes the combined influence of environmental factors and aging degree. These core state indicators can represent the overall operating state of the component with minimal information loss. The core indicators of all components are combined according to a unified preset dimension to form a standardized state feature vector. Each feature vector contains the same number of elements, corresponding to dimensions such as electrical performance, mechanical stability, and thermal management status, allowing direct comparison of the states of different components.

[0069] When constructing the local topology map, the system first identifies directly physically connected component groups in the distribution cabinet. For example, an incoming circuit breaker is connected to a capacitor compensation module via a copper busbar, and this module is connected to multiple outgoing circuit breakers via output copper busbars. These physical connections are obtained from the digital model of the distribution cabinet. For each group of directly connected components, the system calculates the similarity between their state feature vectors. Taking the local group consisting of an incoming circuit breaker, a capacitor compensation module, and two connected outgoing circuit breakers as an example, the system calculates the feature vector similarity between the incoming circuit breaker and the capacitor module, between the incoming circuit breaker and each outgoing circuit breaker, and between the capacitor module and each outgoing circuit breaker. The similarity measure uses cosine similarity calculation; the closer the value is to 1, the more similar the operating states of the two components.

[0070] The system generates connection weights between components based on the calculated similarity metrics. In the local topology graph, the connection weights are set to be proportional to the similarity metrics. For example, if the feature vectors of an incoming circuit breaker and a capacitor compensation module are highly similar, their connection is assigned a higher weight; if the state characteristics of an outgoing circuit breaker differ significantly from those of an incoming circuit breaker, their connection weight is lower. Based on these connection weights, the system draws the topology graph of the local group: each component is represented as a node in the graph, physical connections are represented as edges, and the calculated connection weight values ​​are labeled on the edges.

[0071] The system repeats the above process for all directly physically connected component groups within the distribution cabinet, generating multiple local topology maps. These local topology maps are then merged to form a complete global topology map of the distribution cabinet. In this global map, the state characteristics of each component and its correlation strength with other components are clearly represented, providing a structured relational model for subsequent anomaly detection and maintenance decisions. The entire topology mapping process is fully automated, adapting to changes in component configuration and evolving operating states, maintaining an accurate representation of the system's current state.

[0072] Example 3: See Figure 4 The function of the anomaly marking module is illustrated by processing an example of a power distribution cabinet containing multiple modular components. The global topology map of the power distribution cabinet has been established, which contains several nodes. Each node represents a modular component, such as a circuit breaker, contactor, relay or power module. The edges between nodes represent physical connection relationships, and the edges are assigned weights to represent the correlation strength of state characteristics.

[0073] The process of identifying potentially anomalous component sequences begins by calculating the deviation between the current state feature vector and the historical baseline vector of each modular component. The historical baseline vector is not fixed but dynamically updated based on recent historical operating data of the component under normal operating conditions. For example, for a smart circuit breaker with a rated current of 100A, its historical baseline vector is obtained by taking an exponentially weighted moving average of the state feature vectors from the past 24 hours of operating data. This reflects its normal operating characteristics while slowly adapting to normal drift in equipment state. The current state feature vector is generated in real time from the most recently acquired and processed data. The deviation is calculated using the Mahalanobis distance method, and the formula is as follows:

[0074]

[0075] in, Indicates the first The dynamic deviation of a modular component is a dimensionless scalar value; the larger the value, the greater the deviation of the current state from the normal baseline. Indicates the first The current state feature vector of each component is obtained by feature extraction from real-time data. Indicates the first The historical baseline state feature vector of each component is calculated from historical normal data. Indicates the first The covariance matrix of the eigenvectors of each component under normal conditions describes the correlation between each feature dimension and the normal fluctuation range.

[0076] The system sets an initial deviation threshold for each type of component. This threshold can be adjusted based on the equipment's operating history and on-site conditions. When the deviation of a component... When a preset threshold is exceeded, the system locates the corresponding node of the component in the global topology map of the distribution cabinet and marks it as the initial abnormal node. The system traverses all adjacent nodes of the initial abnormal node. Adjacent nodes refer to other nodes in the global topology map of the distribution cabinet that are directly connected to the initial abnormal node by edges. These edges represent physical connections, such as electrical connections or mechanical linkages. Each edge has a connection weight, which is calculated based on the similarity of state feature vectors when constructing the topology map, representing the correlation strength between the operating states of the components. The system checks whether these connection weights are greater than a preset correlation strength threshold. If the weight of an edge is greater than this threshold, it indicates that the two components are not only physically closely connected but also highly coupled in their operating states. In this case, the abnormal state of the adjacent node is likely related to the initial abnormal node, or it may also have potential problems itself. Therefore, the system marks this adjacent node as an associated abnormal component.

[0077] All tagged components (including the initial anomalous node and associated anomalous components) are integrated to generate a potential anomalous component sequence. This sequence is an ordered list recording all identified potential anomalous components and their corresponding deviation values. When generating a priority change, the system obtains two key data points for each component in the potential anomalous component sequence: its calculated deviation. Secondly, the number of anomalous components associated with it in the global topology graph. (That is, the number of neighboring nodes connected by strongly related edges and also marked as anomalous). Subsequently, a failure risk coefficient is calculated for each potentially anomalous component. The calculation formula is:

[0078]

[0079] This coefficient comprehensively considers the degree of abnormality in the component's own state and the risk of cascading effects that its failure may trigger. A higher value indicates that the component urgently needs to be addressed. The system ultimately classifies all potentially faulty components according to their failure risk coefficient. The components are sorted in descending order of priority, generating a detailed replacement priority list. This list provides a clear basis for maintenance decisions, directing maintenance resources to prioritize the highest-risk and most impactful components, thereby improving maintenance efficiency and system recovery reliability. The entire analysis process runs cyclically, enabling dynamic responses to changes in system status.

[0080] Example 4: The function of the maintenance decision module is illustrated in detail by handling a specific power distribution cabinet maintenance scenario. This power distribution cabinet is installed in an industrial site and has a complex internal structure, containing multiple modular components that require maintenance. The system has generated a priority list containing five components to be replaced, which are distributed in different locations within the cabinet.

[0081] The process of generating the operation instruction set begins with parsing the position coordinates of each component in the replacement priority list within the global topology map of the distribution cabinet. This distribution cabinet uses a three-dimensional coordinate system for spatial positioning, with the lower left corner of the cabinet as the origin. The X-axis represents the horizontal position, the Y-axis represents the vertical position, and the Z-axis represents the depth position. The position coordinates of each component are determined by its specific location on its mounting rail. The system extracts this coordinate information from the digital model, providing a spatial data foundation for subsequent path planning.

[0082] The maintenance strategy library is a knowledge base storing standard disassembly procedures for various component types. This library contains disassembly path templates for multiple component types, such as circuit breaker disassembly templates, contactor disassembly templates, and relay disassembly templates. Each template details the types of tools required for disassembling that type of component, the specific sequence of operating steps, safety precautions, and the estimated operation time. The system automatically matches the corresponding disassembly path template based on the model of the component to be replaced.

[0083] Based on the acquired location coordinates, the system optimizes the tool operation sequence in the disassembly path template. The optimization process considers the spatial distribution of multiple components, aiming to reduce the movement distance of maintenance personnel and the number of tool changes, thereby improving maintenance efficiency. The system analyzes the spatial location of all components to be replaced, calculates the optimal access sequence, and adjusts the tool usage sequence in each disassembly template accordingly. Table 1 shows the basic information of the components to be replaced and the required tool types.

[0084] Table 1: Information on components to be replaced and tool requirements.

[0085]

[0086] When optimizing the disassembly path template, the system first detects the location coordinates of all modular components involved in the current maintenance task. Based on this coordinate information, the system calculates the shortest physical path between each coordinate. The path calculation takes into account the actual layout within the cabinet, including spatial obstacles between components, cable routing, and the width of maintenance aisles. The system employs a path planning algorithm to determine the optimal trajectory for maintenance personnel to move within the cabinet, minimizing duplicate paths and overlapping operations.

[0087] Based on the calculated shortest physical path, the system reorganizes the execution order of the dismantling steps. For example, originally, according to priority, the circuit breaker located at coordinates (450, 1200, 300) should be processed first, followed by the capacitor module located at (300, 900, 250). However, through path optimization, the system may suggest processing the capacitor module first because its location is closer to the maintenance entrance and it is on the same movement path as the subsequent components to be processed.

[0088] The system adds anti-misoperation verification nodes to the disassembly path template. These verification nodes include confirmation steps before key operation procedures, such as power disconnection confirmation, capacitor discharge verification, and line continuity testing. Each verification node contains clear inspection standards and corresponding handling instructions, such as countermeasures for abnormal situations. The final output operation instruction set contains complete maintenance guidance information: a detailed list of the types of tools required and their specifications; step-by-step operation instructions, including disassembly sequence, connector release methods, and handling of mounting fasteners; and safety parameters and precautions, such as torque requirements, insulation class, and safety clearances. The instruction set is presented in structured text format, accompanied by graphical diagrams, to guide maintenance personnel to complete component replacement work safely and efficiently. The entire instruction generation process fully considers the actual on-site operation conditions to ensure the smooth progress of maintenance work.

[0089] Example 5: The maintenance decision module possesses continuous learning and self-optimization capabilities. Deployed in a continuously operating industrial power distribution environment, the various modular components within the distribution cabinet are in a long-term operational state, with their operating parameters and environmental conditions constantly changing over time. The maintenance decision module ensures that its perception of the current operating status remains up-to-date by receiving new data from the status data acquisition module in real time.

[0090] Newly acquired multi-dimensional operational status data is continuously input in a streaming manner. This data includes updated electrical parameter readings, mechanical vibration samples, temperature monitoring values, humidity measurements, and component life assessments updated based on cumulative operating time. Upon data arrival, the system immediately initiates the update process for the equipment status data warehouse. This update process employs an incremental processing mechanism, modifying or adding only changed data records, rather than rebuilding the entire database. Historical data with the earliest timestamps is removed from the current analysis scope according to a preset rolling window strategy, ensuring that the system always analyzes based on the most recent and relevant operational history. The entire update process is completed automatically in the background without interrupting other system functions.

[0091] Once the equipment status data warehouse is updated, the maintenance decision module automatically triggers the recalculation process of the topology mapping module. Upon receiving the update notification, the topology mapping module immediately initiates the regeneration process of the global topology map of the distribution cabinet. This process first performs feature dimensionality reduction on the updated data warehouse and recalculates the status feature vector of each modular component. Since the input data has been updated, these recalculated feature vectors reflect the latest operating status of the components. Subsequently, the system reconstructs the local topology map based on the latest physical connection relationships between adjacent components. Physical connection relationships may change due to maintenance operations or configuration changes, and the system obtains the latest connection information from the digital configuration file of the distribution cabinet. When constructing the local topology map, the system recalculates the similarity measure of the status feature vectors of each component within the same group and generates connection weights between components based on the latest similarity results. All updated local topology maps are finally merged to generate a new global topology map of the distribution cabinet. This new map accurately reflects the current latest status of the system and the latest relationships between components.

[0092] Based on the newly generated global topology map of the distribution cabinet, the maintenance decision module begins iteratively updating the maintenance instruction set. This process first recalculates the deviation between the current state feature vector of each modular component and the updated historical baseline vector. The historical baseline vector is also recalculated as the data warehouse is updated to ensure the timeliness of the comparison benchmark. The system locates component nodes with deviations exceeding a threshold in the new global topology map and traverses the adjacent nodes of these nodes, marking potential abnormal component sequences based on the latest association strength. The system obtains the deviation and the number of associated components for each component in the potential abnormal component sequence, calculates the failure risk coefficient for each component, and generates a new replacement priority list by sorting the components in descending order of risk coefficient.

[0093] The system analyzes the location coordinates of each component in the new replacement priority list within the latest global topology map, matches them with the corresponding disassembly path templates in the maintenance strategy library, and optimizes the operation sequence based on the latest spatial location information, outputting an updated set of operation instructions. This iterative update process forms a complete closed loop, enabling the system to dynamically adapt to changes in the operating status of the distribution cabinet and continuously provide accurate and effective maintenance decision support. The entire update cycle is executed automatically periodically according to system configuration, or triggered immediately upon detecting significant status changes, ensuring that maintenance decisions are always based on the latest system status.

[0094] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0095] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A modular assembly quick change electrical automation switchgear maintenance system, characterized in that, The system comprises: a state data acquisition module, configured to acquire multi-dimensional running state data of each modular component in the power distribution cabinet in real time, perform time alignment and standardization processing on the multi-dimensional running state data, and generate a device state data warehouse; a topology mapping module, configured to perform feature dimension reduction processing on the device state data warehouse, extract state feature vectors of each modular component, construct a local topology graph according to a physical connection relationship between adjacent components, and fuse all local topology graphs to generate a power distribution cabinet global topology graph; an abnormality marking module, configured to calculate a dynamic deviation degree of the state feature vectors of each modular component, combine the correlation strength of adjacent nodes in the power distribution cabinet global topology graph, and mark a potential abnormal component sequence; a maintenance decision module, configured to generate a replacement priority and an operation instruction set of the modular component based on the potential abnormal component sequence and a preset maintenance strategy library; the marking of the potential abnormal component sequence specifically comprises: calculating a deviation degree of a current state feature vector and a historical reference vector of each modular component; locating a component node with a deviation degree exceeding a threshold in the power distribution cabinet global topology graph; traversing adjacent nodes of the node, and marking an associated abnormal component if the correlation strength is greater than a preset value; integrating all marked components to generate a potential abnormal component sequence; the generation of the replacement priority specifically comprises: obtaining a deviation degree and a number of associated components of each component in the potential abnormal component sequence; calculating a component failure risk coefficient according to the product of the deviation degree and the number of associated components; generating a replacement priority list in descending order of the failure risk coefficient.

2. The modular component quick change electrical automation switchgear maintenance system of claim 1, wherein, The multi-dimensional running state data comprises electrical parameter data, mechanical vibration data, temperature distribution data, environmental humidity data and component life data.

3. The modular component quick change electrical automation switchgear maintenance system of claim 1, wherein, The time alignment and standardization processing on the multi-dimensional running state data specifically comprises: interpolating and aligning electrical parameter data, mechanical vibration data, temperature distribution data, environmental humidity data and component life data with different sampling frequencies according to a unified timestamp; performing normalization calculation on each dimension of data respectively to eliminate dimension differences; integrating the normalized multi-dimensional data to form a device state data warehouse.

4. The modular component quick change electrical automation switchgear maintenance system of claim 1, wherein, The extraction of the state feature vectors of each modular component specifically comprises: separating independent running data of each modular component from the device state data warehouse; performing principal component analysis on the independent running data of each modular component respectively to extract core state indicators; combining the core state indicators into state feature vectors according to a preset dimension.

5. The modular component quick change electrical automation switchgear maintenance system of claim 1, wherein, The construction of the local topology graph specifically comprises: identifying modular components groups that are directly physically connected in the power distribution cabinet global topology graph; calculating similarity measures of state feature vectors of components in the same group; generating connection weights between components according to the similarity measures; drawing a local topology graph based on the connection weights.

6. The modular component quick change electrical automation switchgear maintenance system of claim 1, wherein, The generation of the operation instruction set specifically comprises: analyzing position coordinates of each component in the replacement priority list in the power distribution cabinet global topology graph; matching a disassembly path template of a corresponding component type in the maintenance strategy library; optimizing a tool operation sequence in the disassembly path template in combination with the position coordinates; outputting an instruction set containing a tool type, an operation step and a safety parameter.

7. The modular component quick change electrical automation switchgear maintenance system of claim 6, wherein, The optimization of the disassembly path template specifically comprises: detecting position coordinates of a plurality of modular components involved in a current maintenance task; calculating shortest physical paths between the coordinates; reorganizing execution orders of disassembly steps according to the shortest physical paths; adding anti-misoperation verification nodes to the disassembly path template.

8. The modular component quick change electrical automation switchgear maintenance system of claim 1, wherein, The maintenance decision module is further configured to: receive, in real time, newly collected multi-dimensional operation state data to update the equipment state data warehouse; trigger the topology mapping module to regenerate the global topology graph of the power distribution cabinet according to the updated equipment state data warehouse; iteratively update the maintenance instruction set based on the new global topology graph of the power distribution cabinet.

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