An assembly method and platform for a pole-mounted circuit breaker

Abstract

The present invention discloses an assembly method and platform for a pole-mounted circuit breaker, relating to the technical field of low-voltage electrical appliances. The method includes: obtaining components and their connection relationships to identify the assembly neighborhood, and sequentially assembling the components according to a preset assembly scheme. During the assembly process, the components and their neighborhoods are synchronously monitored to generate an assembly monitoring data set. By extracting the monitoring data and performing iterative correlation analysis, the assembly reliability factors are identified. If the reliability factor of a component is lower than the threshold, it is added to the set of abnormal components, and an assembly warning instruction is generated. The present invention solves the technical problems in the prior art that lack the global and sequential analysis of the correlation relationships between the components of the pole-mounted circuit breaker and cannot correct deviations in real time during the assembly process, affecting the assembly efficiency and reliability, and achieves the technical effect of identifying potential assembly problems in real time during the assembly process and correcting them in time to ensure the high efficiency and reliability of the assembly process.

Description

Technical Field

[0001] The present invention relates to the technical field of low-voltage electrical appliances, and particularly relates to an assembly method and platform for a pole-mounted circuit breaker. Background Art

[0002] The pole-mounted circuit breaker is one of the important devices in the power system, mainly used for fault protection such as short circuit and overload in the power line to ensure the safety of power equipment and lines. The pole-mounted circuit breaker consists of multiple components, and the assembly sequence and the correlation relationship between these components are complex. It is difficult for traditional methods to effectively handle the correlation relationship between components. Moreover, the monitoring of the assembly quality of components during the assembly process is often limited to the detection of a single component, lacking a global and sequential analysis of the correlation relationship between components, and it is impossible to correct and optimize in real time during the assembly process. Summary of the Invention

[0003] This application provides an assembly method and platform for a pole-mounted circuit breaker, which are used to solve the technical problems in the prior art that lack a global and sequential analysis of the correlation relationship between the components of the pole-mounted circuit breaker, cannot correct deviations in real time during the assembly process, and affect the assembly efficiency and reliability.

[0004] In the first aspect of this application, an assembly method for a pole-mounted circuit breaker is provided. The method includes: obtaining multiple components of a target pole-mounted circuit breaker, identifying assembly neighborhoods of the multiple components according to the connection relationships between the components in a preset assembly plan to obtain multiple component assembly neighborhoods; sequentially assembling the multiple components according to the preset assembly plan, and synchronously monitoring the multiple components and the multiple component assembly neighborhoods during the assembly process to obtain multiple component assembly monitoring data sets and multiple neighborhood synchronous monitoring data sets; extracting monitoring data from the multiple neighborhood synchronous monitoring data sets according to the sequence of monitoring time windows with the components as indexes to obtain multiple component neighborhood synchronous monitoring data set sequences, where each component neighborhood synchronous monitoring data set sequence corresponds to a component, and each component neighborhood synchronous monitoring data corresponds to a monitoring time window; performing data iterative correlation analysis on the multiple component assembly monitoring data sets and the multiple component neighborhood synchronous monitoring data set sequences to determine multiple component long-time sequential correlation monitoring data sets; traversing the multiple component long-time sequential correlation monitoring data sets to identify assembly reliability factors of the multiple components to obtain multiple component assembly reliability factors; determining whether the multiple component assembly reliability factors are greater than or equal to a preset reliability factor threshold. If not, adding the corresponding components to an abnormal component set, and generating an assembly warning instruction according to the abnormal component set.

[0005] In a second aspect of the present application, an assembly platform for a pole-mounted circuit breaker is provided. The platform includes: an assembly neighborhood recognition module configured to obtain a plurality of components of a target pole-mounted circuit breaker, recognize assembly neighborhoods of the plurality of components according to connection relationships between the components in a preset assembly scheme, and obtain a plurality of component assembly neighborhoods; a synchronous monitoring module configured to sequentially assemble the plurality of components according to the preset assembly scheme, and synchronously monitor the plurality of components and the plurality of component assembly neighborhoods during the assembly process to obtain a plurality of component assembly monitoring data sets and a plurality of neighborhood synchronous monitoring data sets; a monitoring data extraction module configured to extract monitoring data from the plurality of neighborhood synchronous monitoring data sets according to the sequence of monitoring time windows with components as indexes, and obtain a plurality of component neighborhood synchronous monitoring data set sequences, where each component neighborhood synchronous monitoring data set sequence corresponds to a component, and each component neighborhood synchronous monitoring data corresponds to a monitoring time window; an iterative correlation analysis module configured to perform data iterative correlation analysis on the plurality of component assembly monitoring data sets and the plurality of component neighborhood synchronous monitoring data set sequences to determine a plurality of component long-time series correlation monitoring data sets; an assembly reliability factor recognition module configured to traverse the plurality of component long-time series correlation monitoring data sets to recognize assembly reliability factors of the plurality of components and obtain a plurality of component assembly reliability factors; and an assembly warning module configured to determine whether the plurality of component assembly reliability factors are greater than or equal to a preset reliability factor threshold. If not, add the corresponding components to an abnormal component set, and generate an assembly warning instruction according to the abnormal component set.

[0006] One or more technical solutions provided in the present application have at least the following technical effects or advantages:

[0007] An assembly method and platform for a pole-mounted circuit breaker provided in the present application relate to the technical field of low-voltage electrical appliances. By identifying the connection relationships between components and synchronously monitoring the components and neighborhood data during the assembly process, data extraction and iterative analysis are performed to identify assembly reliability factors. If the reliability factor of a certain component is lower than the threshold, it is marked as abnormal and an assembly warning instruction is generated, realizing real-time monitoring and fault warning, solving the technical problem in the prior art that there is a lack of global and time-series analysis of the correlation relationships between the components of the pole-mounted circuit breaker, and it is impossible to correct deviations in real time during the assembly process, affecting the assembly efficiency and reliability, and achieving the technical effect of identifying potential assembly problems in real time during the assembly process and correcting deviations in time to ensure the high efficiency and reliability of the assembly process. Description of the Drawings

[0008] To more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the accompanying drawings required for the description of the embodiments. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other accompanying drawings can be obtained based on these drawings.

[0009] Figure 1 It is a schematic flow chart of an assembly method for a pole-mounted circuit breaker provided by an embodiment of the present application.

[0010] Figure 2 It is a schematic structural diagram of an assembly platform for a pole-mounted circuit breaker provided by an embodiment of the present application.

[0011] Explanation of reference numerals: Assembly neighborhood recognition module 11, synchronous monitoring module 12, monitoring data extraction module 13, iterative correlation analysis module 14, assembly reliability factor recognition module 15, assembly warning module 16. Specific embodiments

[0012] The present application provides an assembly method and platform for a pole-mounted circuit breaker, which are used to solve the technical problems in the prior art that there is a lack of global and sequential analysis of the association relationships between the components of the pole-mounted circuit breaker, and it is impossible to correct deviations in real time during the assembly process, affecting the assembly efficiency and reliability.

[0013] The following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, rather than all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present application.

[0014] It should be noted that the terms "first", "second", etc. in the description and the above accompanying drawings of the present application are used to distinguish similar objects, and do not necessarily need to describe a specific order or sequence. It should be understood that such used data can be interchanged under appropriate circumstances so that the embodiments of the present application described here can be implemented in an order other than those illustrated or described here. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or server including a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or modules not clearly listed or inherent to these processes, methods, products, or devices.

[0015] Embodiment 1, as Figure 1 shown, the present application provides an assembly method for a pole-mounted circuit breaker, and the method includes:

[0016] P10: Obtain multiple components of the target pole-mounted circuit breaker, and identify the assembly neighborhoods of the multiple components according to the connection relationships between the components in the preset assembly plan, so as to obtain multiple component assembly neighborhoods.

[0017] Specifically, it is first necessary to obtain multiple components of the target pole-mounted circuit breaker. A pole-mounted circuit breaker is usually composed of multiple complex mechanical and electrical components, and these components need to be precisely assembled in a specific order and connection relationship during the assembly process. To ensure the efficiency and accuracy of the assembly process, it is necessary to identify the assembly neighborhood of each component according to the preset assembly plan.

[0018] The assembly neighborhood refers to the associated area between a component and other surrounding components during the assembly process. The neighborhood of each component is not only the physically contacting area, but also includes its interaction relationship with other components. To accurately identify the assembly neighborhood of a component, it is first necessary to clarify the connection relationships between the components through the preset assembly plan. In this plan, information such as the function, position, and connection method of each component has been predefined, and this information includes the assembly sequence, connection method, and matching requirements of the components.

[0019] To achieve neighborhood identification, modeling means can be used. For example, by using CAD (Computer-Aided Design) data or 3D modeling technology, each component and its connection relationship are represented in a digital environment. This modeling process can be further optimized through computer vision and image processing technologies to identify the position, orientation, and connection method of the components during the assembly process. In this way, the assembly neighborhoods of multiple components are obtained, and further, the assembly requirements and surrounding environment of each component are evaluated.

[0020] During the assembly process, neighborhood identification is not just a static step, but also needs to be dynamically adjusted in real time according to the assembly progress. For example, when a component has been assembled and connected to other components, the system needs to update the neighborhood information of this component to ensure that subsequent assembled components can be correctly docked with it. Therefore, the identification of the assembly neighborhood not only depends on the preset plan, but also needs to be optimized and adjusted by combining real-time data monitoring and assembly progress.

[0021] P20: Assemble the multiple components in sequence according to the preset assembly plan, and synchronously monitor the multiple components and the multiple component assembly neighborhoods during the assembly process to obtain multiple component assembly monitoring data sets and multiple neighborhood synchronous monitoring data sets.

[0022] Furthermore, step P20 of the embodiment of the present application further includes:

[0023] P21: Serialize the multiple components according to the multiple assembly sequences corresponding to the multiple components in the preset assembly plan to obtain a component sequence; P22: Based on the component sequence and the multiple component assembly neighborhoods, and perform a monitoring sensor array analysis on the multiple assembly positions of the multiple components in the preset assembly plan to obtain a preset monitoring sensor array sequence; P23: Sequentially assemble the components based on the component sequence and the multiple assembly positions, and arrange sensors according to the preset monitoring sensor array sequence during the assembly, and use the arranged preset monitoring sensor array sequence to perform synchronous monitoring to obtain the multiple component assembly monitoring data sets and multiple neighborhood synchronous monitoring data sets.

[0024] It should be understood that the core of the assembly process lies in sequentially assembling multiple components according to a preset assembly plan, and synchronously monitoring each component and its assembly neighborhood during this process, so as to collect assembly monitoring data in real time. The realization of this process not only depends on the precise control of the assembly sequence, but also requires the layout and data collection of the sensor array to ensure the efficiency and quality of the assembly.

[0025] First, serialize the multiple components according to the assembly sequence in the preset assembly plan. The goal of this step is to arrange the components in the assembly in the correct assembly sequence and generate a component sequence. This serialization process is carried out according to the assembly requirements of each component and their connection relationships, ensuring that each component can be assembled in the appropriate sequence. Usually, this process is completed by a dedicated assembly management system, which automatically adjusts the assembly sequence according to the assembly drawings, technical specifications and real-time feedback.

[0026] Next, based on the generated component sequence, combined with the assembly neighborhood of each component (i.e., the mutual relationship area between components) and the assembly position, perform a monitoring sensor array analysis. The assembly position refers to the precise spatial position of each component during the assembly process. Through these positions, it can be determined what kind of sensors are needed for each component for monitoring. The analysis of the monitoring sensor array means reasonably configuring the sensor array according to the assembly sequence and spatial layout of the components, so as to synchronously monitor the state of each component and its neighborhood during the assembly process.

[0027] The sensor array includes sensors such as temperature, pressure, and vibration, which are arranged at key positions where stress, temperature changes or other physical phenomena may occur during the assembly process. Through the array analysis, it can be determined which areas require more monitoring data support, and the sensors can be accurately arranged at these positions.

[0028] After completing the analysis of the monitoring sensor array, it enters the specific operation stage of component assembly. According to the assembly sequence and position, components will be assembled in turn, and in each assembly step, sensors will be arranged according to the preset sensor array sequence. These sensors will work synchronously with the components, continuously collecting data during the assembly process. Synchronous monitoring means that all sensors can work in parallel in real time, continuously tracking the state of the components and their neighborhoods. During the assembly process, the data obtained by the sensors is transmitted to the monitoring system in real time, generating multiple component assembly monitoring data sets and multiple component neighborhood synchronous monitoring data sets. These data sets contain the assembly status information of each component, such as parameters like the accuracy, displacement, stress, and temperature of the component during the assembly process, as well as environmental change information of the surrounding neighborhood, such as temperature fluctuations and pressure changes.

[0029] During the assembly process, the sensor array can be connected to the central control system through wireless network or wired communication to transmit real-time data to the assembly monitoring system. These real-time data can not only help judge the assembly quality but also provide data support for subsequent assembly process optimization and problem correction, improving the efficiency and accuracy of the entire assembly process.

[0030] P30: Indexed by components, monitor data extraction is performed on the multiple neighborhood synchronous monitoring data sets according to the sequence of monitoring time windows, obtaining multiple sequences of component neighborhood synchronous monitoring data sets, where each sequence of component neighborhood synchronous monitoring data sets corresponds to a component, and each component neighborhood synchronous monitoring data corresponds to a monitoring time window.

[0031] Optionally, time series analysis is used to extract the monitoring data in order to refine valuable information related to the component assembly quality from the real-time assembly monitoring data.

[0032] First, indexed by components, extract the multiple neighborhood synchronous monitoring data sets obtained in the previous step according to the sequence of monitoring time windows. The purpose of this process is to organize the dynamic data of each component during the assembly process according to the time sequence, forming an ordered sequence of component neighborhood synchronous monitoring data sets.

[0033] The component neighborhood synchronous monitoring data set contains the real-time monitoring data of each component and its neighborhood. These data usually include the real-time changes of physical parameters such as temperature, vibration, pressure, and stress, as well as possible abnormal signals. To ensure that each component during the assembly process can be effectively monitored at the appropriate moment, it is necessary to organize these monitoring data according to the time sequence of the assembly process, thus forming a complete monitoring data sequence.

[0034] Each neighborhood synchronous monitoring dataset sequence of each component corresponds to all the monitoring data of a specific component during the assembly process. Through the arrangement and sorting of time windows, this dataset sequence reflects the long-term status and dynamic changes of the component during the assembly process. Each data record in each dataset sequence is obtained within a specific monitoring time window, and the data for each time window corresponds to the physical state of the component and its neighborhood at that moment.

[0035] In this way, a complete monitoring time series is established for each component, providing detailed time-based assembly process data support for subsequent analysis. The establishment of the data sequence enables all key links in the assembly process to be systematically tracked and recorded, providing complete historical data for subsequent analysis.

[0036] P40: Perform data iterative correlation analysis on the multiple component assembly monitoring datasets and the multiple neighborhood synchronous monitoring dataset sequences of components to determine the multiple long-time series correlation monitoring datasets of components.

[0037] Furthermore, step P40 of the embodiment of the present application further includes:

[0038] P41: Extract multiple first neighborhood synchronous monitoring datasets of components from the multiple neighborhood synchronous monitoring dataset sequences of components, and perform data iterative correlation analysis with the corresponding multiple component assembly monitoring datasets to obtain multiple first correlation monitoring datasets; P42: Extract multiple second neighborhood synchronous monitoring datasets of components from the multiple neighborhood synchronous monitoring dataset sequences of components again, and perform data iterative correlation analysis with the corresponding multiple second correlation monitoring datasets to obtain multiple second correlation monitoring datasets; P43: After multiple data iterative correlation analyses until reaching the end of the multiple neighborhood synchronous monitoring dataset sequences of components, obtain the multiple long-time series correlation monitoring datasets of components.

[0039] It should be understood that through data iterative correlation analysis, in-depth analysis is performed on the monitoring data of each component and its neighborhood during the assembly process, and finally the long-time series correlation monitoring datasets of components are obtained. The core purpose of data iterative correlation analysis is to identify the long-term correlation laws between components and their neighborhoods during the assembly process through repeated analysis of multiple datasets. This analysis helps to reveal the mutual influence of each component during the assembly process and its long-term impact on the assembly quality, thus providing a strong basis for refined management, assembly optimization, and early warning of potential problems.

[0040] First, extract multiple first component neighborhood synchronous monitoring data sets from a sequence of multiple component neighborhood synchronous monitoring data sets. These data sets contain monitoring information of components and their neighborhoods within a specific time window, such as temperature, pressure, vibration, etc. At this time, the data in the first component neighborhood synchronous monitoring data sets are usually obtained step by step in a time-series manner with the time window as the unit to capture the interaction changes between components and their surrounding environments.

[0041] Next, these extracted neighborhood monitoring data sets will be subjected to data iterative correlation analysis with the corresponding component assembly monitoring data sets. Through this analysis, the component assembly state is combined with the environmental changes in the surrounding neighborhood to identify the dynamic relationship between the component assembly process and neighborhood changes. This process uses various data analysis methods, such as time-series data analysis and data mining techniques, to find the correlation and influence patterns between component assembly and neighborhood data.

[0042] After completing the first data iterative correlation analysis, enter the second round of analysis. At this time, extract multiple second component neighborhood synchronous monitoring data sets from a sequence of multiple component neighborhood synchronous monitoring data sets again, and perform data iterative correlation analysis with the multiple first correlation monitoring data sets obtained previously. In this process, the goal of the second iterative analysis is to further deepen the understanding of the assembly process, especially by multiple iterative analyses to identify potential trends and anomalies in long-term assembly. For example, through data analysis of consecutive time windows, it may be found that there are minor deviations in the long-term assembly of certain components, which are difficult to detect in the short term but will gradually emerge as the data is iterated and correlated.

[0043] After multiple data iterative correlation analyses, gradually deepen until the end of the entire sequence of component neighborhood synchronous monitoring data sets is analyzed. In this process, each iteration will further refine the relationship between data and identify the long-term correlation patterns between each component and its neighborhood. These information finally converge into multiple long time-series correlation monitoring data sets of components, which contain in-depth correlation information between components and their neighborhoods during the assembly process and can provide an accurate data basis for subsequent assembly quality assessment, problem warning, and decision support.

[0044] Furthermore, step P41 of the embodiment of the present application further includes:

[0045] P41-1: Perform inner product mapping recognition on the multiple first component neighborhood synchronous monitoring data sets and the corresponding multiple component assembly monitoring data sets respectively to obtain multiple first data similarity sets; P41-2: Traverse the multiple first data similarity sets for similarity normalization processing, and embed the processing results into an initially empty matrix to obtain multiple first iterative correlation matrices; P41-3: Perform convolution calculation on the multiple first iterative correlation matrices and the multiple first component neighborhood synchronous monitoring data sets to obtain multiple first correlation monitoring data sets.

[0046] In a possible embodiment of the present application, the data analysis process is further refined, especially the correlation analysis between the first component neighborhood synchronous monitoring data set and the component assembly monitoring data set.

[0047] First, perform inner product mapping recognition on the multiple first component neighborhood synchronous monitoring data sets and the corresponding multiple component assembly monitoring data sets respectively. The core task of this step is to measure the similarity between the component assembly data and its neighborhood monitoring data through inner product mapping. Inner product mapping is a mathematical method used to measure the similarity between two vectors in a high-dimensional space. In this step, the component assembly monitoring data and the neighborhood synchronous monitoring data will be transformed into vector form, and each data point contains multi-dimensional features in the time series, such as multiple monitoring dimensions like temperature, pressure, vibration, etc. By calculating the inner product of these data sets, similarity values between the two sets of data can be obtained, that is, multiple first data similarity sets are obtained. These similarity values reflect the dynamic relationship between the component assembly and its neighborhood environment.

[0048] Next, perform similarity normalization processing on the obtained multiple first data similarity sets. Normalization is a common method in data preprocessing, aiming to adjust the data to a standard range to avoid bias in analysis caused by data with different dimensions or numerical ranges. Here, the normalization operation ensures the consistency of data from different components or monitoring time windows in similarity calculation. After the normalization processing is completed, these normalized results are embedded into an initially empty matrix to form multiple first iterative correlation matrices. These matrices will be used for further analysis and provide a data basis for convolution calculation.

[0049] Finally, perform convolution calculations on multiple first iterative correlation matrices and multiple first component neighborhood synchronous monitoring data sets. Convolution is a mathematical operation commonly used in signal processing and image processing. It can perform weighted summation on data through a sliding window method to extract key features in the data. In this step, the purpose of convolution calculation is to combine the correlation matrix with the component neighborhood data to more accurately capture the dynamic changes and related patterns in the assembly process. Through convolution operations, multiple first correlation monitoring data sets can be obtained. These data sets reflect the long-term correlation features between components during the assembly process and the neighborhood data, and can provide a scientific basis for subsequent assembly quality assessment, early warning, and optimization.

[0050] Furthermore, step P41-2 of the embodiment of the present application further includes:

[0051] P41-21: Pre-construct a similarity normalization function, where the similarity normalization function is:

[0052] ; where is the normalized value corresponding to the i-th first data similarity in the first data similarity set, e is the base of the natural logarithm, m is the total number of first data similarities in the first data similarity set, is the first data similarity between the i-th first component neighborhood synchronous monitoring data in the first component neighborhood synchronous monitoring data set and the i-th component assembly monitoring data in the component assembly monitoring data set, is the i-th first component neighborhood synchronous monitoring data in the first component neighborhood synchronous monitoring data set, is the i-th component assembly monitoring data in the component assembly monitoring data set; P41-22: Use the similarity normalization function to perform similarity normalization processing on the multiple first data similarity sets, and embed the processing results into an initially empty matrix to obtain multiple first iterative correlation matrices.

[0053] Specifically, the process of similarity normalization is further refined. The similarity normalization function is used to perform normalization processing on multiple first data similarity sets. The purpose of this process is to convert the similarity values of different component assembly data and neighborhood synchronous monitoring data into a standardized range through an accurate mathematical formula, thereby ensuring the consistency between different data and providing a more reliable input for subsequent correlation analysis and convolution calculations.

[0054] First, a similarity normalization function is pre-constructed for normalizing the similarity values in the first data similarity set. The form of this function is:

[0055] ; where is the normalized value corresponding to the i-th first data similarity in the first data similarity set, e is the base of the natural logarithm, and m is the total number of first data similarities in the first data similarity set. is the first data similarity between the i-th first component neighborhood synchronous monitoring data in the first component neighborhood synchronous monitoring dataset and the i-th component assembly monitoring data in the component assembly monitoring dataset. is the i-th first component neighborhood synchronous monitoring data in the first component neighborhood synchronous monitoring dataset. is the i-th component assembly monitoring data in the component assembly monitoring dataset.

[0056] The similarity normalization function is used to perform similarity normalization processing on multiple first data similarity sets. Each similarity value is mapped to a new range to ensure data consistency and comparability. The processed similarity data will be embedded into an initially empty matrix, which is called the first iterative association matrix. These iterative association matrices are the basic data structures in subsequent analyses and are used to describe the mutual relationships between different component assembly data and neighborhood data. Each matrix element represents the degree of association between a component and its corresponding neighborhood monitoring data. Each row of the matrix represents the assembly process of a component, and the columns represent the corresponding neighborhood data. These matrices provide the necessary basis for subsequent convolution calculations and long-time series data analyses, helping to accurately capture potential anomalies and assembly quality problems in complex assembly processes.

[0057] Furthermore, step P41-3 of the embodiment of the present application further includes:

[0058] P41-31: Using a graph neural network as the basic framework, obtaining multiple sample iterative association matrices, multiple sample first component neighborhood synchronous monitoring datasets, and corresponding multiple sample first association monitoring datasets as training data for the basic framework; P41-32: Based on the training data, using the K-fold cross-validation method to perform supervised training on the basic framework until the training converges to obtain a trained association convolutional network layer; P41-33: Using the association convolutional network layer to perform convolution calculations on the multiple first iterative association matrices and the multiple first component neighborhood synchronous monitoring datasets to obtain multiple first association monitoring datasets.

[0059] Optionally, through the combination of a graph neural network and a convolutional neural network, deep learning processing of the iterative association matrix and component assembly data is performed to further improve the intelligence and accuracy of the assembly process.

[0060] First, construct the graph structure of the assembly data through a graph neural network (GNN). A graph neural network is a deep learning model suitable for processing data with a graph structure, and here it is used to represent the relationship between components and their assembly neighborhoods. Specifically, obtain the iterative correlation matrix of multiple samples (representing the correlation of time-series data between different components), the first component neighborhood synchronous monitoring dataset (recording the data of the assembly neighborhood around the components), and the corresponding first correlation monitoring dataset (representing the correlation relationship between the components and their neighborhood data) as training data. Through these training data, the graph neural network can capture the complex relationships between components and provide a basis for subsequent deep learning analysis.

[0061] Next, based on the collected training data, supervise the training of the graph neural network through the K-fold cross-validation method. K-fold cross-validation is a commonly used machine learning model validation method. It divides the training data into K subsets, trains the model with K-1 subsets each time, and uses the remaining 1 subset for validation. Finally, integrate the validation results of each subset to ensure the generalization ability of the model. Here, the iterative correlation matrix, the component neighborhood synchronous monitoring dataset, and the correlation monitoring dataset are used during the training process to guide the training of the model. The goal is to enable the network to learn the potential rules and features in the component assembly process. Through K-fold cross-validation, the model can reduce the risk of overfitting and improve the robustness of the training process.

[0062] After the training is completed, the obtained correlation convolutional network layer will be used to process new monitoring data. At this stage, use the trained convolutional network layer to perform convolutional calculations on multiple first iterative correlation matrices and the first component neighborhood synchronous monitoring dataset to extract high-level features in the data. Convolutional calculations can help the model identify and extract the spatial and temporal correlations in the assembly process, enabling it to refine the key correlation information from complex time-series data.

[0063] Finally, after convolutional calculations, multiple first correlation monitoring datasets are generated. These datasets contain the key features in the assembly process of each component and its neighborhood, and are used for further analysis and evaluation of the assembly quality. Through this intelligent analysis method based on deep learning, it is possible to more accurately identify and correct problems in the assembly, and improve the reliability and intelligence level of the assembly process.

[0064] P50: Traverse the multiple long-time-series correlation monitoring datasets of the components to identify the assembly reliability factors, and obtain multiple component assembly reliability factors.

[0065] Furthermore, step P50 of the embodiment of the present application further includes:

[0066] P51: Calculate the maximum data difference of the long-time series correlation monitoring datasets of the multiple components respectively to obtain the maximum data differences of the long-time series correlation monitoring data of the multiple components; P52: Traverse the long-time series correlation monitoring datasets of the multiple components to calculate the variances and obtain multiple fluctuation variances; P53: Perform weighted calculation on the maximum data differences of the long-time series correlation monitoring data of the multiple components and the multiple fluctuation variances to obtain the assembly reliability factors of the multiple components.

[0067] It should be understood that by traversing the long-time series correlation monitoring datasets of multiple components and calculating the assembly reliability factors of each component one by one, the reliability of the assembly process can be quantified and evaluated. This process mainly analyzes the time series information in the monitoring data, evaluates the stability and reliability of each component during the assembly process, and identifies the key factors that may affect the assembly quality.

[0068] First, calculate the maximum data differences respectively from the long-time series correlation monitoring datasets of multiple components. The maximum data difference reflects the maximum fluctuation range of each component during the assembly process. Specifically, it is the maximum change range between consecutive monitoring data points of each component during the monitoring period. A larger data difference usually means the instability of the component during the assembly process or possible assembly problems. Therefore, this indicator is an important basis for evaluating the assembly reliability.

[0069] Next, calculate the variances of the long-time series correlation monitoring datasets of multiple components to obtain the fluctuation variances of each component. Variance is a common statistical indicator to measure the fluctuation range of data, which represents the degree of dispersion between the monitoring data points and the data average value. A higher fluctuation variance usually indicates a greater uncertainty in the assembly process of the component, which may affect the final assembly quality. Therefore, variance calculation helps to identify potential quality fluctuation risks in the assembly process.

[0070] Finally, perform weighted calculation on the maximum data difference and the fluctuation variance to obtain the assembly reliability factor of each component. The purpose of weighted calculation is to comprehensively consider the influences of both to obtain a comprehensive reliability evaluation value. Specifically, different weights can be assigned to the maximum difference and the variance to reflect their different influences on the reliability during the assembly process. Through this weighted calculation, the assembly reliability of the component can be more accurately quantified, providing a basis for subsequent assembly warning and quality optimization. This data analysis-based method can identify potential assembly risks in advance, ensure the high precision and high reliability of the assembly process, contribute to optimizing the assembly process and improving the overall product quality.

[0071] P60: Determine whether the assembly reliability factors of the multiple components are greater than or equal to a preset reliability factor threshold. If not, add the corresponding components to the abnormal component set, and generate an assembly warning instruction based on the abnormal component set.

[0072] Optionally, judge the assembly reliability factors of multiple components one by one, specifically judge whether the assembly reliability factor of each component meets the preset reliability factor threshold. This threshold is usually set according to factors such as assembly process requirements, component performance standards, or historical assembly data, etc., to distinguish which components meet the assembly requirements and which components may have assembly risks.

[0073] Specifically, for each component, first obtain its assembly reliability factor, and then compare it with the preset reliability factor threshold. If the assembly reliability factor of this component is greater than or equal to the threshold, it is considered that the reliability of this component during the assembly process meets the requirements, and the assembly process can continue. If the assembly reliability factor is lower than the threshold, it indicates that there may be problems in the assembly process of this component and the reliability is insufficient, and special attention is needed.

[0074] Mark the components with assembly reliability factors lower than the threshold as abnormal components, and add these components to the abnormal component set. The components in this set usually need further inspection and processing to determine the specific reasons for their assembly problems and take necessary corrective measures. For example, it may be necessary to re-evaluate the assembly process, or replace or repair these components.

[0075] Next, generate an assembly warning instruction based on the abnormal component set. This warning instruction is intended to notify relevant personnel (such as assembly workers, quality inspectors, or equipment operators) about the existence of abnormal components, so as to take measures in a timely manner. The warning instruction can include specific descriptions of abnormal components, expected assembly problems, and possible subsequent processing steps, so as to help staff quickly identify problems and take corrective measures to ensure assembly quality.

[0076] In summary, the embodiments of the present application at least have the following technical effects:

[0077] In the present application, by obtaining components and their connection relationships to identify the assembly neighborhood, and sequentially assembling components according to a preset assembly plan, during the assembly process, the components and their neighborhoods are synchronously monitored to generate an assembly monitoring data set. By extracting the monitoring data and performing iterative correlation analysis, the assembly reliability factor is identified. If the component reliability factor is lower than the threshold, it is added to the abnormal component set, and an assembly warning instruction is generated, realizing real-time monitoring and fault warning.

[0078] It achieves the technical effect of real-time identifying potential assembly problems during the assembly process and timely correcting deviations to ensure the efficiency and reliability of the assembly process.

[0079] Embodiment 2. Based on the same inventive concept as the assembly method of a pole-mounted circuit breaker in the foregoing embodiment, as Figure 2 shown, the present application provides an assembly platform for a pole-mounted circuit breaker. The platform in the embodiment of the present application and the method embodiment are based on the same inventive concept. Among them, the platform includes:

[0080] An assembly neighborhood recognition module 11, which is used to obtain a plurality of components of a target pole-mounted circuit breaker, and perform assembly neighborhood recognition on the plurality of components according to the connection relationship between the components in a preset assembly scheme to obtain a plurality of component assembly neighborhoods.

[0081] A synchronous monitoring module 12, which is used to sequentially assemble the plurality of components according to the preset assembly scheme, and synchronously monitor the plurality of components and the plurality of component assembly neighborhoods during the assembly process to obtain a plurality of component assembly monitoring data sets and a plurality of neighborhood synchronous monitoring data sets.

[0082] A monitoring data extraction module 13, which is used to index by components and extract monitoring data from the plurality of neighborhood synchronous monitoring data sets according to the sequence of monitoring time windows to obtain a plurality of component neighborhood synchronous monitoring data set sequences, where each component neighborhood synchronous monitoring data set sequence corresponds to a component, and each component neighborhood synchronous monitoring data corresponds to a monitoring time window.

[0083] An iterative correlation analysis module 14, which is used to perform data iterative correlation analysis on the plurality of component assembly monitoring data sets and the plurality of component neighborhood synchronous monitoring data set sequences to determine a plurality of component long-time series correlation monitoring data sets.

[0084] An assembly reliability factor recognition module 15, which is used to traverse the plurality of component long-time series correlation monitoring data sets to recognize assembly reliability factors and obtain a plurality of component assembly reliability factors.

[0085] An assembly warning module 16, which is used to determine whether the plurality of component assembly reliability factors are greater than or equal to a preset reliability factor threshold. If not, add the corresponding components to the abnormal component set and generate an assembly warning instruction according to the abnormal component set.

[0086] Further, the synchronous monitoring module 12 is further used to perform the following steps:

[0087] Serializing the multiple components according to the multiple assembly sequences corresponding to the multiple components in the preset assembly plan to obtain a component sequence; analyzing a monitoring sensor array based on the component sequence, the multiple component assembly neighborhoods, and the multiple assembly positions of the multiple components in the preset assembly plan to obtain a preset monitoring sensor array sequence; sequentially assembling the components based on the component sequence and the multiple assembly positions, and arranging sensors according to the preset monitoring sensor array sequence during the assembly, and performing synchronous monitoring using the arranged preset monitoring sensor array sequence to obtain the multiple component assembly monitoring data sets and the multiple neighborhood synchronous monitoring data sets.

[0088] Further, the iterative correlation analysis module 14 is further configured to perform the following steps:

[0089] Extracting multiple first component neighborhood synchronous monitoring data sets from the multiple component neighborhood synchronous monitoring data set sequences, performing data iterative correlation analysis with the corresponding multiple component assembly monitoring data sets to obtain multiple first correlation monitoring data sets; extracting multiple second component neighborhood synchronous monitoring data sets from the multiple component neighborhood synchronous monitoring data set sequences again, performing data iterative correlation analysis with the corresponding multiple second correlation monitoring data sets to obtain multiple second correlation monitoring data sets; through multiple data iterative correlation analyses until reaching the end of the multiple component neighborhood synchronous monitoring data set sequences, obtaining the multiple component long-time series correlation monitoring data sets.

[0090] Further, the iterative correlation analysis module 14 is further configured to perform the following steps:

[0091] Performing inner product mapping recognition on the multiple first component neighborhood synchronous monitoring data sets and the corresponding multiple component assembly monitoring data sets respectively to obtain multiple first data similarity sets; traversing the multiple first data similarity sets for similarity normalization processing, and embedding the processing results into an initially empty matrix to obtain multiple first iterative correlation matrices; performing convolution calculation on the multiple first iterative correlation matrices and the multiple first component neighborhood synchronous monitoring data sets to obtain multiple first correlation monitoring data sets.

[0092] Further, the iterative correlation analysis module 14 is further configured to perform the following steps:

[0093] Pre-constructing a similarity normalization function, where the similarity normalization function is:

[0094] ; where is the normalization value corresponding to the i-th first data similarity in the first data similarity set, e is the base of the natural logarithm, and m is the total number of first data similarities in the first data similarity set. Let \(Sim_1(i)\) be the first data similarity between the \(i\)-th first component neighborhood synchronous monitoring data in the first component neighborhood synchronous monitoring dataset and the \(i\)-th component assembly monitoring data in the component assembly monitoring dataset. Let \(x_i\) be the \(i\)-th first component neighborhood synchronous monitoring data in the first component neighborhood synchronous monitoring dataset. Let \(y_i\) be the \(i\)-th component assembly monitoring data in the component assembly monitoring dataset. Use the similarity normalization function to perform similarity normalization on the multiple first data similarity sets, and embed the processing results into an initially empty matrix to obtain multiple first iterative correlation matrices.

[0095] Furthermore, the iterative correlation analysis module 14 is further configured to perform the following steps:

[0096] Taking a graph neural network as the basic framework, obtaining multiple sample iterative correlation matrices, multiple sample first component neighborhood synchronous monitoring datasets, and corresponding multiple sample first correlation monitoring datasets as training data for the basic framework; based on the training data, using the K-fold cross-validation method to perform supervised training on the basic framework until the training converges to obtain a trained correlation convolutional network layer; using the correlation convolutional network layer to perform convolutional calculations on the multiple first iterative correlation matrices and the multiple first component neighborhood synchronous monitoring datasets to obtain multiple first correlation monitoring datasets.

[0097] Furthermore, the assembly reliability factor identification module 15 is further configured to perform the following steps:

[0098] Calculate the maximum data difference of the multiple component long-time series correlation monitoring datasets respectively to obtain multiple maximum differences of component long-time series correlation monitoring data; traverse the multiple component long-time series correlation monitoring datasets to perform variance calculations to obtain multiple fluctuation variances; perform weighted calculations on the multiple maximum differences of component long-time series correlation monitoring data and the multiple fluctuation variances to obtain the multiple component assembly reliability factors.

[0099] It should be noted that the above sequence of embodiments of the present application is only for description and does not represent the superiority or inferiority of the embodiments. And the above describes specific embodiments of this specification. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0100] The above are only the preferred embodiments of the present application and are not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application shall be included within the protection scope of the present application.

[0101] This specification and the drawings are merely exemplary descriptions of the present application and are considered to cover any and all modifications, variations, combinations, or equivalents within the scope of the present application. Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalent technologies, the present application is intended to include these changes and modifications.

Claims

1. A method for assembling a pole mounted circuit breaker, characterized in that: The method comprises: Acquire multiple components of the target pole-mounted circuit breaker, identify assembly neighborhoods of the multiple components according to connection relationships between the components in a preset assembly scheme, and obtain multiple component assembly neighborhoods; Assembling the multiple parts in sequence according to the preset assembly scheme, and synchronously monitoring the multiple parts and the multiple parts assembly neighborhoods during the assembly process to obtain multiple parts assembly monitoring data sets and multiple neighborhood synchronous monitoring data sets; Taking the parts as indexes, extracting monitoring data from the multiple neighborhood synchronous monitoring data sets according to the order of monitoring time windows, and obtaining multiple parts neighborhood synchronous monitoring data set sequences, wherein each part neighborhood synchronous monitoring data set sequence corresponds to a part, and each part neighborhood synchronous monitoring data corresponds to a monitoring time window; Performing data iteration association analysis on the multiple component assembly monitoring data sets and multiple component neighborhood synchronous monitoring data set sequences to determine multiple component long-term association monitoring data sets; Traversing the plurality of component long-term correlation monitoring data sets to identify assembly reliability factors, and obtaining a plurality of component assembly reliability factors; It is determined whether the assembly reliability factors of the plurality of parts are greater than or equal to a preset reliability factor threshold value. If not, the corresponding parts are added into an abnormal parts set, and an assembly warning instruction is generated according to the abnormal parts set.

2. The method for assembling a pole mounted circuit breaker according to claim 1, characterized in that: Performing data iteration association analysis on the multiple component assembly monitoring data sets and multiple component neighborhood synchronous monitoring data set sequences to determine multiple component long-term association monitoring data sets, including: Extracting a plurality of first component neighborhood synchronous monitoring data sets from the plurality of component neighborhood synchronous monitoring data set sequences, and performing data iterative association analysis with the corresponding plurality of component assembly monitoring data sets to obtain a plurality of first associated monitoring data sets; Extracting a plurality of second component neighborhood synchronous monitoring data sets from the plurality of component neighborhood synchronous monitoring data set sequences again, and performing data iterative association analysis with the corresponding plurality of second associated monitoring data sets to obtain a plurality of second associated monitoring data sets; After multiple data iteration association analyses, until the end of the plurality of component neighborhood synchronous monitoring data set sequences is reached, the plurality of component long-term association monitoring data sets are obtained.

3. The method for assembling a pole mounted circuit breaker according to claim 2, characterized in that: Extracting a plurality of first component neighborhood synchronous monitoring data sets from the plurality of component neighborhood synchronous monitoring data set sequences, and performing data iterative association analysis with the corresponding plurality of component assembly monitoring data sets to obtain a plurality of first associated monitoring data sets, including: Performing inner product mapping identification on the plurality of first component neighborhood synchronous monitoring data sets and the corresponding plurality of component assembly monitoring data sets respectively to obtain a plurality of first data similarity sets; Traversing the plurality of first data similarity sets to perform similarity normalization processing, and embedding the processing results into an initially empty matrix to obtain a plurality of first iterative association matrices; Convolution calculation is performed on the multiple first iterative association matrices and the multiple first component neighborhood synchronous monitoring data sets to obtain multiple first associated monitoring data sets.

4. The method for assembling a pole mounted circuit breaker according to claim 3, characterized in that: include: A similarity normalization function is pre-constructed, wherein the similarity normalization function is: ； in, is the normalized value corresponding to the i-th first data similarity in the first data similarity set, e is the base of the natural logarithm, m is the total number of first data similarities in the first data similarity set, is the first data similarity between the i-th first component neighborhood synchronous monitoring data in the first component neighborhood synchronous monitoring data set and the i-th component assembly monitoring data in the component assembly monitoring data set, is the i-th first component neighborhood synchronous monitoring data in the first component neighborhood synchronous monitoring data set, It is the i-th component assembly monitoring data in the component assembly monitoring data set; The similarity normalization function is used to perform similarity normalization processing on the multiple first data similarity sets, and the processing results are embedded in an initially empty matrix to obtain multiple first iterative correlation matrices.

5. The method for assembling a pole mounted circuit breaker according to claim 3, characterized in that: include: Taking the graph neural network as the basic framework, multiple sample iterative association matrices and multiple sample first component neighborhood synchronous monitoring data sets, as well as corresponding multiple sample first association monitoring data sets are obtained as training data for the basic framework; Based on the training data, the basic framework is supervised trained using a K-fold cross validation method until the training converges, thereby obtaining a trained associated convolutional network layer; The association convolutional network layer is used to perform convolution calculations on the multiple first iterative association matrices and the multiple first component neighborhood synchronous monitoring data sets to obtain multiple first association monitoring data sets.

6. The method for assembling a pole mounted circuit breaker according to claim 1, characterized in that: Traversing the plurality of component long-term correlation monitoring data sets to identify assembly reliability factors, and obtaining a plurality of component assembly reliability factors, including: Calculate the maximum data difference of the plurality of component long-series correlation monitoring data sets respectively to obtain the maximum data difference of the plurality of component long-series correlation monitoring data sets; Traversing the plurality of component long-term correlation monitoring data sets to perform variance calculation to obtain a plurality of fluctuation variances; The maximum difference of the long-term correlated monitoring data of the multiple components and the multiple fluctuation variances are weightedly calculated to obtain the assembly reliability factors of the multiple components.

7. The method for assembling a pole mounted circuit breaker according to claim 1, characterized in that: The plurality of parts are assembled sequentially according to the preset assembly scheme, and the plurality of parts and the plurality of parts assembly neighborhoods are synchronously monitored during the assembly process to obtain a plurality of parts assembly monitoring data sets and a plurality of neighborhood synchronous monitoring data sets, including: According to a plurality of assembly sequences corresponding to the plurality of components in the preset assembly scheme, serializing the plurality of components to obtain a component sequence; Performing a monitoring sensor array analysis based on the component sequence and the plurality of component assembly neighborhoods, and the plurality of assembly positions of the plurality of components in the preset assembly scheme to obtain a preset monitoring sensor array sequence; Based on the component sequence and multiple assembly positions, components are assembled in sequence, and sensors are arranged according to the preset monitoring sensor array sequence during assembly. Synchronous monitoring is performed using the arranged preset monitoring sensor array sequence to obtain the multiple component assembly monitoring data sets and multiple neighborhood synchronous monitoring data sets.

8. An assembly platform for a pole-mounted circuit breaker, characterized in that: The platform includes: An assembly neighborhood identification module, the assembly neighborhood identification module is used to obtain multiple parts of the target pole-mounted circuit breaker, and perform assembly neighborhood identification on the multiple parts according to the connection relationship between the parts in the preset assembly scheme to obtain multiple parts assembly neighborhoods; A synchronous monitoring module, wherein the synchronous monitoring module is used to assemble the plurality of parts in sequence according to the preset assembly scheme, and synchronously monitor the plurality of parts and the plurality of parts assembly neighborhoods during the assembly process to obtain a plurality of parts assembly monitoring data sets and a plurality of neighborhood synchronous monitoring data sets; A monitoring data extraction module, wherein the monitoring data extraction module is used to extract monitoring data from the multiple neighborhood synchronous monitoring data sets based on the order of monitoring time windows with the parts as indexes, and obtain multiple parts neighborhood synchronous monitoring data set sequences, wherein each part neighborhood synchronous monitoring data set sequence corresponds to a part, and each part neighborhood synchronous monitoring data corresponds to a monitoring time window; An iterative association analysis module, the iterative association analysis module is used to perform data iterative association analysis on the multiple component assembly monitoring data sets and the multiple component neighborhood synchronous monitoring data set sequences to determine multiple component long-term association monitoring data sets; An assembly reliability factor identification module, wherein the assembly reliability factor identification module is used to traverse the plurality of component long-term correlation monitoring data sets to perform assembly reliability factor identification and obtain a plurality of component assembly reliability factors; The assembly warning module is used to determine whether the assembly reliability factors of the multiple parts are greater than or equal to a preset reliability factor threshold. If not, the corresponding parts are added to the abnormal parts set, and an assembly warning instruction is generated according to the abnormal parts set.

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