Railway wagon configuration history management method, device, medium and apparatus

Abstract

The application discloses a railway freight car configuration history management method, device, medium and equipment, and the method comprises the following steps: acquiring history basic information, including the attributes and attribute fields of each configuration item. An association matrix is constructed according to the correlation intensity of each attribute field, the penetration probability between each attribute field is determined based on the association matrix, the attribute grouping and the history type identification of the attribute field are determined according to the penetration probability. Each classified attribute field is taken as a node to construct a causal reasoning graph, and the causal strength and the causal path between each classified attribute field are determined according to the causal reasoning graph. The classified attribute fields are established into a hierarchical data structure according to a preset configuration level, the causal strength and the causal path between the classified attribute fields are marked in the hierarchical data structure, and the attribute configuration result of each configuration item is obtained. The application is helpful for the effective management and tracing of the attribute information of the railway freight car configuration item.

Description

Technical Field

[0001] This application belongs to the field of configuration management technology, and in particular relates to a method, device, medium and equipment for railway freight car configuration history management. Background Technology

[0002] During the operation and maintenance of railway freight cars, it is necessary to manage the attribute information of each configuration item throughout its entire lifecycle. Traditional freight car configuration history management methods lack effective attribute classification standards and data organization methods, making it difficult to effectively manage and trace attribute information. Summary of the Invention

[0003] The embodiments of this application provide a method, apparatus, medium, and equipment for railway freight car configuration history management, which can at least to some extent facilitate the effective management and traceability of attribute information of railway freight car configuration items by forming a hierarchical attribute configuration structure.

[0004] Other features and advantages of this application will become apparent from the following detailed description, or may be learned in part from practice of this application.

[0005] The first aspect of this application provides a method for managing the configuration history of railway freight cars, wherein the railway freight cars include a plurality of configuration items, and the method includes: Obtain basic resume information, which includes attribute fields for each configuration item, and each configuration item includes at least one attribute field; A correlation matrix is ​​constructed based on the correlation strength of each attribute field. The penetration probability between any two attribute fields is determined based on the correlation matrix. The attribute grouping and resume type identifier of the attribute fields are determined based on the penetration probability. The penetration probability is the probability that one attribute field can be reached from another attribute field in the correlation matrix. A causal reasoning graph is constructed using each categorized attribute field as a node. The causal strength and causal path between each categorized attribute field are determined based on the causal reasoning graph. The categorized attribute fields have attribute grouping information and history type identification information. Each categorized attribute field is used to establish a hierarchical data structure according to a preset configuration level. The causal strength and causal path between the categorized attribute fields are marked in the hierarchical data structure to obtain the attribute configuration results of each configuration item.

[0006] Optionally, constructing the association matrix based on the correlation strength of the attribute fields includes: Each attribute field is transformed into the frequency domain and the time domain respectively to obtain the corresponding frequency domain feature vector and the time domain feature vector; Calculate the first correlation strength between each pair of frequency domain feature vectors and the second correlation strength between each pair of time domain feature vectors; The correlation matrix is ​​established based on the first correlation strength and the second correlation strength.

[0007] Optionally, calculating the first correlation strength between any two frequency domain feature vectors and the second correlation strength between any two time domain feature vectors includes: At least one correlation strength algorithm is used to calculate the first correlation strength between any two frequency domain feature vectors and the second correlation strength between any two time domain feature vectors. The at least one correlation strength algorithm includes at least one of the Pearson correlation coefficient algorithm, mutual information value algorithm, and distance correlation algorithm.

[0008] Optionally, determining the penetration probability between any two of the attribute fields based on the association matrix includes: The preset influence strength of the correlation matrix is ​​used as the initial transmission probability. The probability decay value between each pair of attribute fields on the transmission path is calculated through iterative accumulation. The difference between the initial transmission probability and the probability decay value is used as the penetration probability.

[0009] Optionally, determining the attribute grouping and resume type identifier of the attribute field based on the penetration probability includes: Obtain multiple penetration probabilities between one of the attribute fields and each of the other attribute fields, and construct a probability density curve based on the multiple penetration probabilities; Identify each cluster in the probability density curve, and classify each attribute field corresponding to the same cluster into the same attribute group, wherein the similarity of each penetration probability within each cluster is greater than or equal to a preset similarity. Obtain the overlap degree of the penetration path between the attribute group and each preset resume type identifier, and take the resume type identifier with the largest overlap degree as the resume type identifier of the attribute field.

[0010] Optionally, the step of constructing a causal reasoning graph using each categorized attribute field as a node includes: An initial inference graph is constructed using the categorized attribute fields as nodes, where the initial weights of the edges between nodes are used to reflect the initial association strength between nodes; Obtain a weight correction index, and correct the initial weight of the initial inference graph according to the weight correction index to obtain the causal inference graph. The weight correction index includes at least one of the following: historical value change records of the attribute field, extraction time lag, and change correlation degree; wherein, the greater the time lag of the historical value extracted later, the smaller the change correlation degree between it and the historical value extracted earlier.

[0011] Optionally, after obtaining the attribute configuration results of each of the configuration items, the method further includes: Obtain the attribute field sharing degree between each pair of the aforementioned resume type identifiers. The attribute field sharing degree is the ratio between the attribute fields commonly associated between each pair of resume type identifiers and the total number of attribute fields of the two resume type identifiers. A directed graph is constructed using the resume type identifier as nodes, and the connection relationships between nodes, the weights of the connecting edges, and the switching directions between nodes are determined based on the attribute field sharing degree. Generate a resume type switcher between each pair of resume type identifiers based on the directed graph.

[0012] Optionally, after generating a resume type switcher between each pair of resume type identifiers based on the directed graph, the method further includes: In response to a user's view switching operation, the resume type view is switched from the current resume type view to the target resume type view based on the resume type switcher.

[0013] Optionally, after obtaining the attribute configuration results of each of the configuration items, the method further includes: In the hierarchical data structure, data binding relationships are established between related attribute field nodes based on a publish-subscribe mechanism.

[0014] Optionally, after obtaining the attribute configuration results of each of the configuration items, the method further includes: Obtain the metadata of the attribute field and the data source of the metadata, and establish a mapping relationship between the data source and the metadata; A multi-level tree-like index structure is established according to the data source type. The index structure includes upper-level nodes, intermediate nodes, and lower-level nodes. The upper-level nodes are used to represent the data source type and the identifier of the instance data corresponding to the data source. The intermediate nodes are used to represent the database of the instance data. The lower-level nodes are used to represent the metadata of the attribute fields.

[0015] Optionally, after establishing a multi-level tree-like index structure according to the data source type, the method further includes: Obtain the update status bitmap of each attribute field of the hierarchical data structure, where each bit of the update status bitmap corresponds to one attribute field, and the bit value of the bit is used to indicate whether the attribute field has been updated. If the attribute field is updated, the target data source corresponding to the metadata of the attribute field is obtained based on the index structure, the updated attribute field is obtained from the target data source, and the updated attribute field is synchronized to the display view of the hierarchical data structure.

[0016] Optionally, after obtaining the attribute configuration results of each of the configuration items, the method further includes: In response to a user's resume tracing request, the target resume information corresponding to the resume tracing request is obtained from the hierarchical data structure.

[0017] Optionally, obtaining the target resume information corresponding to the resume tracing request from the hierarchical data structure includes: Based on preset inverted index rules and / or jump structures, the target resume information corresponding to the resume tracing request is obtained from the hierarchical data structure.

[0018] Optionally, after synchronizing the updated attribute fields to the display view of the hierarchical data structure, the method further includes: The update event sequence corresponding to the attribute field is obtained by sorting the update timestamps of the attribute fields. Associate the update event sequence with the attribute field nodes in the hierarchical data structure.

[0019] Optionally, after obtaining the attribute configuration results of each of the configuration items, the method further includes: Based on the correlation between the attribute fields in the hierarchical data structure across different dimensions, a multi-dimensional view is displayed for the hierarchical data structure. The multi-dimensional view includes at least one of the following: time dimension view, structure dimension view, attribute dimension view, and event dimension view.

[0020] A second aspect of this application provides a railway freight car configuration history management device, wherein the railway freight car includes a plurality of configuration items, and the device includes: The acquisition unit is used to acquire basic resume information, which includes attribute fields for each configuration item, and each configuration item includes at least one attribute field. The first determining unit is configured to construct an association matrix based on the correlation strength of each attribute field, determine the penetration probability between any two attribute fields based on the association matrix, and determine the attribute grouping and resume type identifier of the attribute fields based on the penetration probability, wherein the penetration probability is the probability that one attribute field can be reached from another attribute field in the association matrix. The second determining unit is used to construct a causal reasoning graph using each categorized attribute field as a node, and to determine the causal strength and causal path between each categorized attribute field based on the causal reasoning graph. The categorized attribute fields have attribute grouping information and history type identification information. The result obtaining unit is used to establish a hierarchical data structure for each categorized attribute field according to a preset configuration level, and to mark the causal strength and causal path between the categorized attribute fields in the hierarchical data structure to obtain the attribute configuration result of each configuration item.

[0021] A third aspect of this application provides a computer-readable storage medium storing at least one computer program instruction, which is loaded and executed by a processor to perform the operations described in any of the methods described in the first aspect.

[0022] A fourth aspect of this application provides an electronic device including one or more processors and one or more memories, wherein at least one piece of program code is stored in the one or more memories, and the at least one piece of program code is loaded and executed by the one or more processors to perform the operation as described in any of the methods in the first aspect.

[0023] The one or more technical solutions provided in the embodiments of the present invention achieve at least the following technical effects or advantages: The railway freight car configuration history management method of this application includes: acquiring basic history information, which includes attributes and attribute fields for each configuration item, with each attribute including at least one attribute field; constructing an association matrix based on the correlation strength of each attribute field; determining the penetration probability between any two attribute fields based on the association matrix; determining the attribute grouping and history type identifier of the attribute fields based on the penetration probability, where the penetration probability is the probability that one attribute field can be reached from another attribute field in the association matrix; constructing a causal reasoning graph using each categorized attribute field as a node; determining the causal strength and causal path between each categorized attribute field based on the causal reasoning graph, wherein the categorized attribute fields have attribute grouping information and history type identifier information; establishing a hierarchical data structure for each categorized attribute field according to a preset configuration hierarchy; marking the causal strength and causal path between the categorized attribute fields in the hierarchical data structure to obtain the attribute configuration results for each configuration item. Therefore, this application embodiment, by forming a hierarchical attribute configuration structure, facilitates the effective management and traceability of attribute information for railway freight car configuration items, such as clustering and displaying attribute fields from different history type views.

[0024] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Attached Figure Description

[0025] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application. It is obvious that the drawings described below are merely some embodiments of this application, and those skilled in the art can derive other drawings from these drawings without creative effort. In the drawings: Figure 1 A flowchart illustrating the railway freight car configuration history management method according to an embodiment of this application is shown; Figure 2 A structural diagram of a railway freight car configuration history management device according to an embodiment of this application is shown; Figure 3 A schematic diagram of the structure of a computer system suitable for implementing the electronic device of the present application is shown. Detailed Implementation

[0026] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0027] Furthermore, the described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. Numerous specific details are provided in the following description to give a thorough understanding of embodiments of this application. However, those skilled in the art will recognize that the technical solutions of this application can be practiced without one or more of the specific details, or other methods, components, apparatuses, steps, etc., can be employed. In other instances, well-known methods, apparatuses, implementations, or operations are not shown or described in detail to avoid obscuring various aspects of this application.

[0028] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different models and / or processor devices and / or microcontroller devices.

[0029] The flowcharts shown in the accompanying drawings are merely illustrative and do not necessarily include all content and operations / steps, nor do they necessarily have to be performed in the described order. For example, some operations / steps can be broken down, while others can be combined or partially combined; therefore, the actual execution order may change depending on the specific circumstances.

[0030] It should also be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such uses of these terms can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described.

[0031] The configuration item of a railway freight car can refer to the item established to distinguish and define the differences in structural form, functional configuration, technical parameters and application categories of railway freight cars, and it runs through the entire life cycle of freight car design, manufacturing, operation and maintenance.

[0032] For example, the configuration items of a railway freight car may include: the basic shape of the car body, the running gear and load-bearing structure, dimensions and load parameters, functions and auxiliary equipment, etc. Among these, the basic shape of the car body can be... Open wagons without roofs, boxcars with doors and windows, flatcars with only a floor, and special tank cars, etc.; the running gear and load-bearing structure can be the number of bogie axles (such as two-axle and three-axle), the body load-bearing method (such as underframe load-bearing or integral load-bearing), and the model of the coupler buffer device; the size and load-bearing parameters can be the vehicle distance, vehicle length, width and maximum height, tare weight, and maximum load, etc.; the functions and auxiliary equipment can be configured according to the needs of transporting goods, such as hoppers, multi-layer racks, special binding devices, insulation or pressurization systems, etc.

[0033] During the operation and maintenance of railway freight cars, it is necessary to manage the attribute information of each configuration item throughout its entire lifecycle. Traditional freight car configuration history management methods lack effective attribute classification standards and data organization methods, making it difficult to effectively manage and trace attribute information.

[0034] In view of this, embodiments of this application provide a railway freight car configuration history management method. This method, at least to a certain extent, helps to effectively manage and trace the attribute information of railway freight car configuration items by forming a hierarchical attribute configuration structure, such as clustering and displaying attribute fields of different history type views.

[0035] The railway freight car configuration history management method of this application embodiment will be described below with reference to the accompanying drawings.

[0036] Figure 1 A flowchart illustrating a railway freight car configuration history management method according to an embodiment of this application is shown.

[0037] The first aspect of this application provides a method for managing the configuration history of railway freight cars, wherein the railway freight cars include a plurality of configuration items, and the method includes, but is not limited to: Step S10. Obtain basic resume information, wherein the basic resume information includes attribute fields for each configuration item, and each configuration item includes at least one attribute field; Understandably, based on the attribute definition requirements of railway freight car configuration items, the parameter values ​​of each attribute can be obtained in different business intervals. These business intervals include, but are not limited to, design, manufacturing, inspection, operation and maintenance, and scrapping. For each business interval, the values ​​of relevant attributes of the configuration item within that interval can be obtained, forming a parameter value sequence. Furthermore, the degree of difference in the values ​​of the same attribute across different business intervals can be calculated. For example, the theoretical value of the failure rate in the design phase might be 0.05%, but in the actual application phase it might be 0.08%. In the next phase, the theoretical design value can be adjusted based on this difference.

[0038] For example, configuration items can be car body, frame assembly, sidewall assembly, endwall assembly, bogie, wheelset assembly, bolster, side frame, brake shoes, coupler, coupler buffer device, etc. The attribute fields of each configuration item include, but are not limited to: name, function identifier, definition, function, performance parameters, specifications, and interface, etc., where the name is, for example, car body; the function identifier is, for example, BA; the definition is, for example, that the car body is the main load-bearing structure of the vehicle; the function is, for example, carrying cargo and connecting various components of the vehicle; the performance parameters are, for example, length, width, height, and material; and the interface is, for example, connection to the bogie via a center pin.

[0039] Understandably, by analyzing the commonalities and correlations among various attribute fields, an attribute grouping mechanism can be established in advance. For example, multiple attribute groups can be established based on functions, installation locations, etc., and each attribute field can be categorized into its corresponding attribute group later.

[0040] Step S20. Construct an association matrix based on the correlation strength of each attribute field, determine the penetration probability between any two attribute fields based on the association matrix, and determine the attribute grouping and resume type identifier of the attribute fields based on the penetration probability, wherein the penetration probability is the probability that one attribute field can be reached from another attribute field in the association matrix; In some embodiments, constructing the association matrix based on the correlation strength of the attribute fields includes: Step S201. Perform frequency domain and time domain transformation on each attribute field to obtain the corresponding frequency domain feature vector and time domain feature vector; Step S202. Calculate the first correlation strength between each pair of frequency domain feature vectors and the second correlation strength between each pair of time domain feature vectors; Step S203. Establish the correlation matrix based on the first correlation strength and the second correlation strength.

[0041] Understandably, in steps S201-S203, each attribute field undergoes signal processing, transforming it to the frequency domain using Fourier transforms and other methods to obtain a frequency domain feature vector reflecting its periodic fluctuation pattern, such as the main frequency component and amplitude. Simultaneously, the statistical characteristics of the attribute fields in the time dimension (e.g., mean, variance, peak factor, and waveform factor) are calculated to form a time domain feature vector. Next, the similarity of the frequency domain feature vectors between each pair of attribute fields is calculated to obtain the first correlation strength. This first correlation strength reveals the association of attributes in periodic change patterns (e.g., whether an anomaly in a certain vibration frequency is synchronized with the wear cycle of a specific component). The similarity between each pair of time domain feature vectors is calculated to obtain the second correlation strength, which reflects the immediate association of attributes in numerical change trends (e.g., the direct relationship between increased mileage and increased failure rate). Combining these two different dimensions of correlation strength (e.g., taking a weighted average), an association matrix is ​​constructed for each attribute field. This association matrix depicts the association of each attribute field of the configuration term in both the time and frequency domains.

[0042] In some embodiments, calculating the first correlation strength between any two frequency domain feature vectors and the second correlation strength between any two time domain feature vectors includes: At least one correlation strength algorithm is used to calculate the first correlation strength between any two frequency domain feature vectors and the second correlation strength between any two time domain feature vectors; wherein, the at least one correlation strength algorithm includes at least one of the Pearson correlation coefficient algorithm, the mutual information value algorithm, and the distance correlation algorithm.

[0043] It is understandable that the principles for calculating the first and second correlation strengths based on the Pearson correlation coefficient algorithm, mutual information value algorithm, and distance correlation algorithm can be found in relevant technologies, and will not be elaborated here.

[0044] In some embodiments, determining the penetration probability between any two of the attribute fields based on the association matrix includes: The preset influence strength of the correlation matrix is ​​used as the initial transmission probability. The probability decay value between each pair of attribute fields on the transmission path is calculated through iterative accumulation. The difference between the initial transmission probability and the probability decay value is used as the penetration probability.

[0045] Understandably, the association matrix can be viewed as a network graph, with each attribute field representing a node, and the strength of the association matrix's influence being the weight of the edges connecting the nodes. Furthermore, by setting a relevance threshold, weak connections can be filtered out, resulting in a clear and effective relationship network graph.

[0046] Based on the filtered network graph, penetration is achieved through initial propagation probability and multi-level indirect paths (e.g., A affects B, and B then affects C). The influence originating from a node in a certain attribute field will spread along all possible paths, but it will decay according to the edge weight (i.e., probability decay value) each time it passes through an attribute field node. Therefore, the final penetration probability from one attribute node to another, i.e., the initial propagation probability minus the probability decay value, represents the total probability that a state change in one attribute node will eventually affect another attribute node after considering all possible paths. It quantifies indirect and potential dependencies.

[0047] In some embodiments, determining the attribute grouping and resume type identifier of the attribute field based on the penetration probability includes: Step S204. Obtain multiple penetration probabilities between one of the attribute fields and each of the other attribute fields, and construct a probability density curve based on the multiple penetration probabilities; Step S205. Identify each cluster in the probability density curve, and classify each attribute field corresponding to the same cluster into the same attribute group, wherein the similarity of each penetration probability in each cluster is greater than or equal to a preset similarity. Step S206. Obtain the overlap degree of the penetration path between the attribute group and each preset resume type identifier, and take the resume type identifier with the largest overlap degree as the resume type identifier of the attribute field.

[0048] Based on the preceding text, for a given attribute field, we can analyze its penetration probability distribution to all other attribute fields in the network diagram and plot it as a probability density curve. The peaks on this curve represent clusters of attribute fields closely related to and easily influenced by that attribute field; the troughs represent the boundary points of the penetration probability. By identifying the troughs, the attribute network can be segmented at weak points in influence transmission, thus objectively dividing attribute groups into those with strong internal correlations and those with weak external influences.

[0049] Taking a railway freight car bogie system as an example, its attribute network includes mechanical attributes such as axle box temperature, wheelset vibration frequency, and frame stress, as well as management attributes such as maintenance mileage and last maintenance item. After calculating the penetration probability from axle box temperature to all other attributes and plotting the density curve, the curve may show that within the mechanical attribute group (such as between temperature, vibration, and stress), the probability value is high and continuous, forming a peak area; while when reaching management attributes such as maintenance mileage, the probability value decreases, forming a trough. This trough is the boundary of the attribute group. Cutting at this point objectively divides the attributes into a mechanical state group and a maintenance record group, thereby achieving dynamic grouping.

[0050] It should be noted that the resume type identifier is used to indicate the resume type. Resume types can be classified according to different business stages. For example, resume type identifiers include: design identifier, operation and maintenance identifier, application identifier, manufacturing identifier, and scrap identifier.

[0051] It should be noted that penetration path overlap refers to the degree of overlap, path length, and intensity of all impact penetration paths between fields within an attribute group and the preset resume type standard fields. A higher overlap indicates a better match between the group and the corresponding resume type's business association and impact pattern.

[0052] Understandably, each attribute group is treated as a whole, and the overlap of penetration paths between all attribute fields within that group and the standard attribute field sets contained in each preset resume type is calculated. The degree of matching and coverage of the impact on the penetration path from the grouped attributes to a certain resume type is evaluated, and the resume type with the highest overlap is assigned to that attribute group.

[0053] Step S30. Construct a causal reasoning graph using each categorized attribute field as a node, and determine the causal strength and causal path between each categorized attribute field based on the causal reasoning graph. The categorized attribute fields have attribute grouping information and history type identification information. In some embodiments, constructing a causal reasoning graph using each categorized attribute field as a node includes: Step S301. Construct an initial inference graph using the categorized attribute fields as nodes, where the initial weights of the edges between nodes are used to reflect the initial association strength between nodes; Step S302. Obtain weight correction index, and correct the initial weight of the initial inference graph according to the weight correction index to obtain the causal inference graph. The weight correction index includes at least one of the following: historical value change records of the attribute field, extraction time lag degree, and change correlation degree; wherein, the greater the time lag degree of the historical value extracted later, the smaller the change correlation degree between it and the historical value extracted earlier.

[0054] In steps S301 and S302, each attribute field that has completed attribute grouping and history type classification is used as a node, and an initial weight is preset. This weight initially reflects the initial correlation strength between attribute fields. Multi-dimensional weight correction indicators are introduced to refine the initial graph. These indicators include: historical value change records, used to confirm whether the correlation has a stable collaborative change pattern; extracted time lag, used to identify the temporal order of changes in two attribute values; a larger lag usually indicates a lower causal probability; and change correlation degree, used to quantify the degree of correlation between subsequent changes and previous changes; the later the change, the weaker the correlation between it and the earlier the change. By combining these indicators, the initial weights are strengthened, weakened, or reconstructed, ultimately correcting the initial inference graph, which only reflects correlation, into a causal inference graph that better reveals directional causal relationships.

[0055] Understandably, based on the constructed causal reasoning graph, by identifying the propagation level of each attribute field, this propagation level is used as the path for back-calculating causal strength. This allows for the calculation of the influence between upstream and downstream nodes along the causal path, quantifying the causal strength between two fields. In some embodiments, causal strength can also distinguish between direct dependencies and transitive dependencies. For example, a direct dependency is a relationship directly connected by a single edge, while a transitive dependency is a relationship corresponding to multiple paths through intermediate nodes. For transitive dependency paths, the cumulative value of the causal strength of each edge along the entire path can be calculated as the total strength of the path. Thus, important transitive paths with cumulative causal strength exceeding a threshold can be retained, forming dependency chains together with direct dependencies, fully explaining the transmission chain from the root cause to the final state.

[0056] Step S40. Establish a hierarchical data structure for each categorized attribute field according to a preset configuration level, and mark the causal strength and causal path between the categorized attribute fields in the hierarchical data structure to obtain the attribute configuration result of each configuration item.

[0057] For example, the preset configuration level can be component level, system level, or vehicle level.

[0058] Understandably, a tree-like or nested hierarchical data structure is constructed using a pre-defined configuration hierarchy at the component, system, and vehicle levels. Each attribute field, after being categorized and analyzed for causality, is placed under its corresponding hierarchical node according to the physical entity it describes (e.g., wheel diameter at the component level, braking system efficiency at the system level). In this hierarchical data structure, not only are the attribute fields themselves stored, but causal strength and causal paths are also marked. For example, the causal link between bearing temperature at the component level and bogie vibration at the system level is explicitly marked and stored. Thus, a complete data structure integrating physical hierarchy, attributes, and causal dependencies constitutes a traceable and reasonable attribute configuration result for configuration items.

[0059] In some embodiments, after obtaining the attribute configuration results of each of the configuration items, the method further includes: Step S501. Obtain the attribute field sharing degree between each pair of resume type identifiers, wherein the attribute field sharing degree is the ratio between the attribute fields commonly associated between each pair of resume type identifiers and the total number of attribute fields of the two resume type identifiers; Step S502. Construct a directed graph using the resume type identifier as nodes, and determine the connection relationship between nodes in the directed graph, the weight of the connection edge, and the switching direction between nodes based on the attribute field sharing degree; Step S503. Generate a resume type switcher between each pair of resume type identifiers based on the directed graph.

[0060] Understandably, in steps S501-S503, the attribute field sharing degree is the proportion of the number of attribute fields that two history type identifiers share common interest in relative to the total number of attribute fields in both. This sharing degree quantifies the degree of data overlap and correlation between business views. A directed graph is constructed using history type identifiers as nodes, and the connection relationships between nodes, edge weights, and connection directions are determined by the calculated sharing degree. For example, design and manufacturing views may share a large number of drawing parameters, thus exhibiting high sharing degree and a natural flow from design to manufacturing. A visual history type switcher is generated based on this directed graph. This switcher can recommend and highlight the most closely related attributes and switch to other business views based on the currently viewed attributes and analysis context, achieving data penetration and correlation analysis across lifecycle stages.

[0061] In some embodiments, after generating a resume type switcher between each pair of the resume type identifiers based on the directed graph, the method further includes: In response to a user's view switching operation, the resume type view is switched from the current resume type view to the target resume type view based on the resume type switcher.

[0062] Understandably, when a user is analyzing the current view of a configuration item (e.g., design history), if they want to explore its status in another business stage (e.g., maintenance history), they can trigger a switching operation through the interface. Through the navigation logic encoded in the underlying directed graph, the currently displayed data panel, attribute fields, and correlation analysis are switched from the design history view to the maintenance history view, thereby enabling cross-business stage data viewing and analysis. The history type switcher allows for real-time switching between different history types (e.g., design, manufacturing, maintenance) on the same interface, improving information readability and interactive experience.

[0063] When displaying the view, the data feature values ​​and business feature values ​​under the set of attribute fields requested by the user can be displayed. The data feature values ​​include, but are not limited to, indicators such as data type and value range, while the business feature values ​​include, but are not limited to, indicators such as business category and usage frequency.

[0064] In some embodiments, after obtaining the attribute configuration results of each of the configuration items, the method further includes: In the hierarchical data structure, data binding relationships are established between related attribute field nodes based on a publish-subscribe mechanism.

[0065] For example, data binding relationships are established between attribute field nodes at different levels, such as component level and system level, based on the publish-subscribe observer pattern, thereby establishing a data dependency and linkage mechanism. When the value of an attribute field acting as a publisher changes, this change event is automatically pushed or notified to all relevant subscriber nodes that have subscribed to that data, according to the marked causal path. This mechanism ensures that data changes in the causal network can be transmitted and take effect in real time and automatically.

[0066] In some embodiments, after obtaining the attribute configuration results of each of the configuration items, the method further includes: Step S601. Obtain the metadata of the attribute field and the data source of the metadata, and establish a mapping relationship between the data source and the metadata; Step S602. Establish a multi-level tree-shaped index structure according to the data source type. The index structure includes upper-level nodes, intermediate nodes, and lower-level nodes. The upper-level nodes are used to represent the data source type and the identifier of the instance data corresponding to the data source. The intermediate nodes are used to represent the database of the instance data. The lower-level nodes are used to represent the metadata of the attribute fields.

[0067] In steps S601-S602, the metadata of the attribute fields includes, but is not limited to, unique identifiers, names, and data types. A mapping relationship is established between the attribute field metadata and the data source identifier, for example, establishing a mapping relationship between the attribute field identifier and the data source identifier, thereby enabling the management and access of the attribute field's data source. Specifically, a multi-level tree index is constructed according to the data source type (e.g., design database, real-time operation and maintenance stream): the top-level node of the index distinguishes the data source category and specific instance; the middle-level nodes point to the specific database or storage entity under that instance; and the bottom-level nodes are associated with the specific attribute field metadata or its storage path. This logically organizes the scattered data, enabling rapid location of the definition of any attribute and its actual data location within the data source.

[0068] In some embodiments, after establishing a multi-level tree-like index structure according to the data source type, the method further includes: Step S603. Obtain the update status bitmap of each attribute field of the hierarchical data structure, where each bit of the update status bitmap corresponds to one attribute field, and the bit value of the bit is used to indicate whether the attribute field has been updated. Step S604. If the attribute field is updated, obtain the target data source corresponding to the metadata of the attribute field based on the index structure, obtain the updated attribute field from the target data source, and synchronize the updated attribute field to the display view of the hierarchical data structure.

[0069] In steps S603-S604, an update status bitmap is used as a lightweight change trigger. A binary bitmap is maintained for all attribute fields in the hierarchical data structure, where each bit corresponds to a field. When the underlying actual data of any field changes (e.g., a sensor uploads a new reading or a new maintenance record is entered into the database), its corresponding bit value is set. By scanning this bitmap, the data synchronization process is initiated for those fields marked as updated.

[0070] Specifically, based on a multi-level tree index, the address of the target data source mapped to the metadata of the field is quickly located, and the latest data value is retrieved from that source (such as a specific database table or data stream). The retrieved updated value is synchronized back to the corresponding attribute field node in the hierarchical data structure, and a partial refresh of the displayed view is triggered, thereby ensuring that the project status seen in the user interface is consistent with the multi-source data in the background, improving synchronization efficiency.

[0071] For example, by maintaining a field mapping table, each attribute field displayed on the front end corresponds to a data source in the back end (such as a business system or database table). Based on the field mapping table, a data access proxy is created for each displayed attribute field. This proxy understands and encapsulates the complex logic of accessing a specific backend data source. Field update status bitmaps are registered to both the front-end display module and the backend data source listener. When backend data changes, triggered by the bitmap, the proxy can automatically retrieve the new data according to the mapping path and push it to the front end; conversely, when the front end modifies data, it can also synchronize back to the data source through the proxy, thus building a two-way data synchronization channel and achieving synchronous linkage between the display and source data.

[0072] In some embodiments, after obtaining the attribute configuration results of each of the configuration items, the method further includes: In response to a user's resume tracing request, the target resume information corresponding to the resume tracing request is obtained from the hierarchical data structure.

[0073] For example, when a user requests a history tracing report on abnormal vibration of a truck's bogie, the system responds by searching the hierarchical data structure of the bogie type item, locating the vibration frequency attribute field, and aggregating and tracing all related target history information upwards along the pre-marked causal path and data binding relationship in the data structure. This includes, but is not limited to, real-time monitoring data under the maintenance identifier, maintenance records under the historical inspection identifier, and the inherent frequency parameters of this type of bogie under the design identifier. This integrates this information across the lifecycle and business stages into a complete fault tracing report and presents it to the user.

[0074] When tracing historical records, the tracing methods can be divided into forward tracing and backward tracing. Forward tracing aims to demonstrate the evolution process. When a user chooses forward tracing (for example, to see how a design parameter affects subsequent versions), starting from the node corresponding to the initial state, the query path is constructed level by level along the reference relationships of child nodes in the data structure (such as the next version, derived model), forming a forward evolution path on the timeline, intuitively showing the state changes and iteration process of the configuration item from the past to the present. Backward tracing, on the other hand, aims to uncover historical causes. When a user needs to analyze the root cause of a current fault state, the path is constructed level by level backward along the parent node or the backward reference of the causal dependency edge (such as the previous maintenance stage, the design source), thus clearly revealing the historical changes, decisions, and event chains that led to the current state.

[0075] In some embodiments, obtaining the target resume information corresponding to the resume tracing request from the hierarchical data structure includes: Based on preset inverted index rules and / or jump structures, the target resume information corresponding to the resume tracing request is obtained from the hierarchical data structure.

[0076] Understandably, an inverted index rule refers to creating a reverse index for each attribute field, directly pointing to all historical records and node positions containing that attribute field, enabling fast queries by finding records based on values. A jump structure is a pre-built shortcut path index within a tree hierarchy. For example, it can directly link from a specific bolt model at the component level to all related system-level maintenance records. Its function is to quickly jump across multiple intermediate levels, shortening the traversal path and thus improving query efficiency.

[0077] In some embodiments, after synchronizing the updated attribute fields to the display view of the hierarchical data structure, the method further includes: Step S701. Sort the update event sequence corresponding to the attribute field according to the timestamp of the attribute field update; Step S702. Associate the update event sequence with the attribute field nodes in the hierarchical data structure.

[0078] In steps S701-S702, all change events are read and sorted by timestamp to form a historical change event sequence. The correlation is comprehensively judged by analyzing their temporal sequence and periodicity, whether they belong to the same physical component or system, and whether they have semantic business logic connections. Based on these multi-dimensional relationships, an event association graph is generated to reveal the potential impact chains between events. Each configuration-level node is associated with the relevant event sequence, and traversed using inverted indexes and skip lists to clearly trace the complete path of the historical event influence on the node.

[0079] In some embodiments, after obtaining the attribute configuration results of each of the configuration items, the method further includes: Based on the correlation between the attribute fields in the hierarchical data structure across different dimensions, a multi-dimensional view is displayed for the hierarchical data structure. The multi-dimensional view includes at least one of the following: time dimension view, structure dimension view, attribute dimension view, and event dimension view.

[0080] Understandably, the time-dimensional view displays the historical value sequence and change events of attribute fields in a timeline format. The structure-dimensional view organizes and displays attributes according to the physical hierarchy of components, systems, and the entire vehicle. The attribute-dimensional view clusters highly correlated attributes based on correlation or causality strength. The event-dimensional view focuses on the historical change event sequence and the relationship diagram between them. These views are not isolated but originate from the same underlying data, ensuring data consistency during multi-perspective analysis. Users can approach the data from different dimensions to gain a comprehensive understanding of the status and evolution of configuration items.

[0081] Figure 2 This is a structural diagram of the railway freight car configuration history management device according to an embodiment of this application.

[0082] A second aspect of this application provides a railway freight car configuration history management device 200, wherein the railway freight car includes a plurality of configuration items, and the device 200 includes: The acquisition unit 201 is used to acquire basic resume information, the basic resume information including attribute fields for each configuration item, and each configuration item including at least one attribute field; The first determining unit 202 is used to construct an association matrix based on the correlation strength of each attribute field, determine the penetration probability between any two attribute fields based on the association matrix, and determine the attribute grouping and resume type identifier of the attribute fields based on the penetration probability, wherein the penetration probability is the probability that one attribute field can be reached from another attribute field in the association matrix. The second determining unit 203 is used to construct a causal reasoning graph using each categorized attribute field as a node, and to determine the causal strength and causal path between each categorized attribute field according to the causal reasoning graph, wherein the categorized attribute fields have attribute grouping information and history type identification information. The resulting unit 204 is used to establish a hierarchical data structure for each categorized attribute field according to a preset configuration level, and to mark the causal strength and causal path between the categorized attribute fields in the hierarchical data structure to obtain the attribute configuration result of each configuration item.

[0083] A third aspect of this application provides a computer-readable storage medium storing at least one computer program instruction, which is loaded and executed by a processor to perform the operations as described in any of the methods in the first aspect.

[0084] Computer-readable storage media may be portable compact disc read-only memory (CD-ROM) and include program code, and may run on a terminal device, such as a personal computer. However, the computer-readable storage medium of this application is not limited thereto. In this application, the readable storage medium may be any tangible medium that contains or stores a program that may be used by or in conjunction with an instruction execution system, apparatus, or device.

[0085] Readable storage media can be, for example, but not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatuses, or devices, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: electrical connections having one or more wires, portable disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, and portable compact disk read-only memory (CD). ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0086] Program code for performing the operations of this application can be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java and C++, and conventional procedural programming languages ​​such as C or similar languages. The program code can execute entirely on the user's computing device, partially on the user's device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0087] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual couplings, direct couplings, or communication connections may be through some interfaces; indirect couplings or communication connections between units or modules may be electrical or other forms.

[0088] The units described as separate components may or may not be physically separate. Similarly, the components of the control device may or may not be physical units; that is, they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0089] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0090] Figure 3 A schematic diagram of the structure of a computer system suitable for implementing the electronic device of the present application is shown.

[0091] According to a fourth aspect of the present application, an electronic device is provided, including one or more processors and one or more memories, wherein at least one piece of program code is stored in the one or more memories, the at least one piece of program code being loaded and executed by the one or more processors to perform the operations performed as described in any of the methods in the first aspect.

[0092] like Figure 3 As shown, the electronic device 400 is manifested in the form of a general-purpose computing device. The components of the electronic device 400 may include, but are not limited to: at least one processing unit 410, at least one storage unit 420, and a bus 430 connecting different system components (including storage unit 420 and processing unit 410).

[0093] The storage unit stores program code, which can be executed by the processing unit 410, causing the processing unit 410 to perform the steps described in the "Embodiment Method" section above according to various exemplary embodiments of this application.

[0094] Storage unit 420 may include readable media in the form of volatile storage units, such as random access memory (RAM) 421 and / or cache 422, and may further include read-only memory (ROM) 423.

[0095] Storage unit 420 may also include a program / utility 424 having a set (at least one) of program modules 425, such program modules 425 including but not limited to: an operating system, one or more application programs, other program modules, and program data, each or some combination of these examples may include an implementation of a network environment.

[0096] Bus 430 can represent one or more of several bus structures, including a memory cell bus or memory cell controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local bus using any of the multiple bus structures.

[0097] Electronic device 400 can also communicate with one or more external devices 500 (e.g., keyboard, pointing device, Bluetooth device, etc.), and with one or more devices that enable a user to interact with electronic device 400, and / or with any device that enables electronic device 400 to communicate with one or more other computing devices (e.g., router, modem, etc.). This communication can be performed through I / O (input / output) interface 450, which can also be connected to display unit 440 to display the communication content. Furthermore, electronic device 400 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public network, such as the Internet) through network adapter 460. As shown, network adapter 460 communicates with other modules of electronic device 400 via bus 430. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with electronic device 400, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0098] The above description is merely an embodiment of this application and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A method for managing the configuration history of railway freight cars, characterized in that, The railway freight car includes several configuration items, and the method includes: Obtain basic resume information, which includes attribute fields for each configuration item, and each configuration item includes at least one attribute field; A correlation matrix is ​​constructed based on the correlation strength of each attribute field. The penetration probability between any two attribute fields is determined based on the correlation matrix. The attribute grouping and resume type identifier of the attribute fields are determined based on the penetration probability. The penetration probability is the probability that one attribute field can be reached from another attribute field in the correlation matrix. A causal reasoning graph is constructed using each categorized attribute field as a node. The causal strength and causal path between each categorized attribute field are determined based on the causal reasoning graph. The categorized attribute fields have attribute grouping information and history type identification information. Each categorized attribute field is used to establish a hierarchical data structure according to a preset configuration level. The causal strength and causal path between the categorized attribute fields are marked in the hierarchical data structure to obtain the attribute configuration results of each configuration item.

2. The method according to claim 1, characterized in that, The step of constructing the association matrix based on the correlation strength of the attribute fields includes: Each attribute field is transformed into the frequency domain and the time domain respectively to obtain the corresponding frequency domain feature vector and the time domain feature vector; Calculate the first correlation strength between each pair of frequency domain feature vectors and the second correlation strength between each pair of time domain feature vectors; The correlation matrix is ​​established based on the first correlation strength and the second correlation strength.

3. The method according to claim 1, characterized in that, The calculation of the first correlation strength between each pair of frequency domain feature vectors and the second correlation strength between each pair of time domain feature vectors includes: At least one correlation strength algorithm is used to calculate the first correlation strength between any two frequency domain feature vectors and the second correlation strength between any two time domain feature vectors. The at least one correlation strength algorithm includes at least one of the Pearson correlation coefficient algorithm, mutual information value algorithm, and distance correlation algorithm.

4. The method according to claim 1, characterized in that, The determination of the penetration probability between any two attribute fields based on the association matrix includes: The preset influence strength of the correlation matrix is ​​used as the initial transmission probability. The probability decay value between each pair of attribute fields on the transmission path is calculated through iterative accumulation. The difference between the initial transmission probability and the probability decay value is used as the penetration probability.

5. The method according to claim 1 or 4, characterized in that, The step of determining the attribute grouping and resume type identifier of the attribute field based on the penetration probability includes: Obtain multiple penetration probabilities between one of the attribute fields and each of the other attribute fields, and construct a probability density curve based on the multiple penetration probabilities; Identify each cluster in the probability density curve, and classify each attribute field corresponding to the same cluster into the same attribute group, wherein the similarity of each penetration probability within each cluster is greater than or equal to a preset similarity. Obtain the overlap degree of the penetration path between the attribute group and each preset resume type identifier, and take the resume type identifier with the largest overlap degree as the resume type identifier of the attribute field.

6. The method according to claim 1, characterized in that, The construction of a causal reasoning graph using each categorized attribute field as a node includes: An initial inference graph is constructed using the categorized attribute fields as nodes, where the initial weights of the edges between nodes are used to reflect the initial association strength between nodes; Obtain a weight correction index, and correct the initial weight of the initial inference graph according to the weight correction index to obtain the causal inference graph. The weight correction index includes at least one of the following: historical value change records of the attribute field, extraction time lag, and change correlation degree; wherein, the greater the time lag of the historical value extracted later, the smaller the change correlation degree between it and the historical value extracted earlier.

7. The method according to any one of claims 1-6, characterized in that, After obtaining the attribute configuration results of each configuration item, the method further includes: Obtain the attribute field sharing degree between each pair of the aforementioned resume type identifiers. The attribute field sharing degree is the ratio between the attribute fields commonly associated between each pair of resume type identifiers and the total number of attribute fields of the two resume type identifiers. A directed graph is constructed using the resume type identifier as nodes, and the connection relationships between nodes, the weights of the connecting edges, and the switching directions between nodes are determined based on the attribute field sharing degree. Generate a resume type switcher between each pair of resume type identifiers based on the directed graph.

8. The method according to claim 7, characterized in that, After generating a resume type switcher between each pair of resume type identifiers based on the directed graph, the method further includes: In response to a user's view switching operation, the resume type view is switched from the current resume type view to the target resume type view based on the resume type switcher.

9. The method according to any one of claims 1-6, characterized in that, After obtaining the attribute configuration results of each configuration item, the method further includes: In the hierarchical data structure, data binding relationships are established between related attribute field nodes based on a publish-subscribe mechanism.

10. The method according to claim 1, characterized in that, After obtaining the attribute configuration results of each configuration item, the method further includes: Obtain the metadata of the attribute field and the data source of the metadata, and establish a mapping relationship between the data source and the metadata; A multi-level tree-like index structure is established according to the data source type. The index structure includes upper-level nodes, intermediate nodes, and lower-level nodes. The upper-level nodes are used to represent the data source type and the identifier of the instance data corresponding to the data source. The intermediate nodes are used to represent the database of the instance data. The lower-level nodes are used to represent the metadata of the attribute fields.

11. The method according to claim 10, characterized in that, After establishing a multi-level tree-like index structure according to the data source type, the method further includes: Obtain the update status bitmap of each attribute field of the hierarchical data structure, where each bit of the update status bitmap corresponds to one attribute field, and the bit value of the bit is used to indicate whether the attribute field has been updated. If the attribute field is updated, the target data source corresponding to the metadata of the attribute field is obtained based on the index structure, the updated attribute field is obtained from the target data source, and the updated attribute field is synchronized to the display view of the hierarchical data structure.

12. The method according to claim 1, characterized in that, After obtaining the attribute configuration results of each configuration item, the method further includes: In response to a user's resume tracing request, the target resume information corresponding to the resume tracing request is obtained from the hierarchical data structure.

13. The method according to claim 12, characterized in that, The step of obtaining the target resume information corresponding to the resume tracing request from the hierarchical data structure includes: Based on preset inverted index rules and / or jump structures, the target resume information corresponding to the resume tracing request is obtained from the hierarchical data structure.

14. The method according to claim 11, characterized in that, After synchronizing the updated attribute fields to the display view of the hierarchical data structure, the method further includes: The update event sequence corresponding to the attribute field is obtained by sorting the update timestamps of the attribute fields. Associate the update event sequence with the attribute field nodes in the hierarchical data structure.

15. The method according to claim 1, characterized in that, After obtaining the attribute configuration results of each configuration item, the method further includes: Based on the correlation between the attribute fields in the hierarchical data structure across different dimensions, a multi-dimensional view is displayed for the hierarchical data structure. The multi-dimensional view includes at least one of the following: time dimension view, structure dimension view, attribute dimension view, and event dimension view.

16. A railway freight car configuration history management device, characterized in that, The railway freight car includes several configuration items, and the device includes: The acquisition unit is used to acquire basic resume information, which includes attribute fields for each configuration item, and each configuration item includes at least one attribute field. The first determining unit is configured to construct an association matrix based on the correlation strength of each attribute field, determine the penetration probability between any two attribute fields based on the association matrix, and determine the attribute grouping and resume type identifier of the attribute fields based on the penetration probability, wherein the penetration probability is the probability that one attribute field can be reached from another attribute field in the association matrix. The second determining unit is used to construct a causal reasoning graph using each categorized attribute field as a node, and to determine the causal strength and causal path between each categorized attribute field based on the causal reasoning graph. The categorized attribute fields have attribute grouping information and history type identification information. The result obtaining unit is used to establish a hierarchical data structure for each categorized attribute field according to a preset configuration level, and to mark the causal strength and causal path between the categorized attribute fields in the hierarchical data structure to obtain the attribute configuration result of each configuration item.

17. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores at least one computer program instruction, which is loaded and executed by a processor to perform the operation as described in any one of claims 1-15.

18. An electronic device, characterized in that, It includes one or more processors and one or more memories, wherein at least one piece of program code is stored in the one or more memories, and the at least one piece of program code is loaded and executed by the one or more processors to perform the operation performed by the method as described in any one of claims 1-15.

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