A vehicle multi-data association display system and method

Abstract

The present application relates to the field of data analysis processing, and more particularly to a vehicle multi-data correlation display system and method, the present application determines the interference characteristics of each type of fault by obtaining the operation data of several types of faults corresponding to the vehicle; and evaluates the operation interference representation parameters of each type of fault in combination with the repair cost, sets the interference label of the type fault; identifies frequent information items based on several information items corresponding to the type fault, determines the push rule according to the co-occurrence frequency between the frequent information items and pushes to the vehicle display end; calls the operation data of the corresponding type fault, detects the type fault according to the interference label; and synchronously updates and pushes according to the repair frequency, under the premise of real-time evaluation of the influence of the fault on the vehicle operation efficiency, the present application timely updates the fault data of the vehicle and pushes to the vehicle display end, which is convenient for relevant personnel to clearly understand the correlation between the fault data of the vehicle, and is convenient for making quick and accurate decisions, and ensures the safe and stable operation of the vehicle.

Description

Technical Field

[0001] This invention relates to the field of data analysis and processing, and in particular to a system and method for displaying multi-data association of vehicles. Background Technology

[0002] With the widespread use of subways in cities, subway vehicles may experience some malfunctions that affect their normal operation during long-term operation. Therefore, it is necessary to conduct a specific analysis of these malfunctions in order to better understand their nature and scope of impact.

[0003] The development of big data technology has provided strong support for the storage, management and analysis of massive amounts of fault information. Through the big data platform, fault data of subway vehicles can be collected, stored and processed efficiently, providing a data foundation for correlation display. Learning algorithms provide strong support for the analysis and mining of fault information, and then correlation analysis and integration display of a large amount of data can be carried out to facilitate subsequent fault detection and analysis.

[0004] Chinese Patent Application Publication No. CN115423557A discloses a method for associating and displaying bidding data, including the following steps: S1, establishing rules for acquiring enterprise bidding data, acquiring enterprise bidding data and related enterprise information, and cleaning the acquired data; S2, classifying the acquired enterprise information data and enterprise bidding data according to information dimensions, including general bidding information dimensions, bidding-specific information dimensions, and bidding-change information dimensions; S3, displaying the processed enterprise data and bidding data using one or more dimensions, solving the current problems of low correlation between bidding information and overly fragmented bidding data; compared with existing data association and display methods, this solution is more applicable to the bidding industry and can make the current bidding data more clearly and hierarchically displayed.

[0005] However, the following problems still exist in the existing technology.

[0006] Traditional methods for displaying subway fault information typically present fault data in a simple and direct report format. While this provides basic fault information, it fails to demonstrate the relationships between data within the fault information. When faced with complex fault situations, relevant personnel need to spend a significant amount of time and effort to analyze and understand the data, making it difficult to make quick and accurate decisions and impacting the safe and stable operation of the trains. Summary of the Invention

[0007] To address this issue, the present invention provides a vehicle multi-data association display system and method to overcome the problem that the existing technology cannot display the association between data within the fault information when displaying subway fault information. When faced with complex fault situations, relevant personnel need to spend a lot of time and energy to analyze and understand the faults, making it difficult to make accurate decisions quickly and affecting the safe and stable operation of the vehicles.

[0008] To achieve the above objectives, the present invention provides a method for displaying multiple vehicle data associations, comprising:

[0009] Obtain operational data for several types of vehicle faults to determine the interference characteristics of each type of fault, including the number of affected nodes and the duration of vehicle downtime;

[0010] Based on the interference characteristics and maintenance costs, the operational interference characterization parameters of each type of fault are evaluated to set interference labels for that type of fault;

[0011] Based on several information items corresponding to the aforementioned type of fault, frequent information items are identified, and push rules are determined and pushed to the vehicle display terminal based on the co-occurrence frequency among the frequent information items.

[0012] The system calls up the operation data of the corresponding type of fault, and performs detection and analysis on the type of fault based on the interference label. This includes determining the occurrence frequency and adjacent occurrence duration of the type of fault within a predetermined time period, so as to evaluate the fault density characterization value of the corresponding type of fault, to determine whether the type of fault has reached the fault threshold, and adjusting the maintenance frequency of the component corresponding to the type of fault based on the fault density characterization value.

[0013] Updates will be pushed synchronously based on the aforementioned maintenance frequency.

[0014] Furthermore, the process of evaluating the operational interference characterization parameters of each type of fault based on the interference characteristics and maintenance costs includes,

[0015] The sum of the ratio of the number of affected nodes to the threshold of the number of affected nodes and the ratio of the vehicle stagnation time to the threshold of the vehicle stagnation time is used as the first operational interference feature.

[0016] The ratio of maintenance cost to maintenance cost threshold is used as the second operational interference feature;

[0017] The weighted sum of the first operational interference feature and the second operational interference feature is used as the operational interference characterization parameter.

[0018] Furthermore, setting interference labels for the fault types includes,

[0019] If the operational interference characterization parameter of any type of fault is greater than or equal to the operational interference characterization parameter threshold, then a severe interference label is set for that type of fault.

[0020] Furthermore, the process of identifying frequent information items includes,

[0021] Determine the word frequency of several information items;

[0022] Information items whose word frequency is greater than or equal to the word frequency threshold are identified as frequent information items.

[0023] Furthermore, the process of determining push rules based on the co-occurrence frequency among the frequent information items includes,

[0024] Cluster the frequent information items that meet the frequent co-occurrence criteria;

[0025] The frequently occurring information items after clustering are used as the main title of the push notification;

[0026] Obtain the semantic similarity between the remaining frequent information items (excluding the main push title) and each of the main push titles;

[0027] Sort the semantic similarities in descending order;

[0028] The remaining frequently occurring information items are placed under the main title of the push notification corresponding to the first item in the descending sort as the push notification subtitle;

[0029] The frequent co-occurrence criteria include a co-occurrence frequency greater than or equal to a co-occurrence frequency threshold.

[0030] Furthermore, based on the interference label, the type of fault is detected and analyzed, including...

[0031] If any type of fault with a severely interfering label is found, then the fault type will be detected and analyzed.

[0032] Furthermore, the process of evaluating the fault density characterization values ​​for the corresponding type of fault includes,

[0033] The ratio of occurrence frequency to occurrence frequency threshold is used as the first fault density feature;

[0034] The ratio of the adjacency occurrence duration threshold to the adjacency occurrence duration is used as the second fault density feature;

[0035] The sum of the first fault-dense feature and the second fault-dense feature is taken as the fault-dense characterization value.

[0036] Further, determining whether the aforementioned type of fault has reached a fault threshold includes,

[0037] If the fault density representation value of any type of fault is greater than or equal to the fault density representation threshold, then the fault of that type is determined to have reached the fault threshold.

[0038] Furthermore, adjusting the maintenance frequency of components corresponding to the type of fault based on the fault density characteristic value includes,

[0039] The maintenance frequency is positively correlated with the fault density characteristic value.

[0040] Furthermore, a system for displaying multi-data association of vehicles is also provided, characterized by including:

[0041] The data acquisition module is used to acquire operational data of several types of faults corresponding to the vehicle, so as to determine the interference characteristics of each type of fault, including the number of affected nodes and the vehicle's downtime.

[0042] An interference assessment module, connected to the data acquisition module, is used to assess the operational interference characterization parameters of each type of fault based on the interference characteristics and maintenance costs, so as to set the interference labels for the type of fault.

[0043] The associated push module is connected to the data acquisition module and is used to identify frequent information items based on several information items corresponding to the type of fault, determine push rules based on the co-occurrence frequency between the frequent information items, and push them to the vehicle display terminal.

[0044] The operation analysis module, which is connected to the interference evaluation module, is used to call the operation data of the corresponding type of fault, and to detect and analyze the type of fault according to the interference label. This includes determining the occurrence frequency and adjacent occurrence duration of the type of fault within a predetermined time period, evaluating the fault density characterization value of the corresponding type of fault, determining whether the type of fault has reached the fault threshold, and adjusting the maintenance frequency of the component corresponding to the type of fault according to the fault density characterization value.

[0045] The push update module is connected to the operation analysis module and is used to push updates synchronously according to the maintenance frequency.

[0046] Compared with existing technologies, this invention obtains operational data of several types of vehicle faults to determine the interference characteristics of each type of fault; evaluates the operational interference characterization parameters of each type of fault based on the interference characteristics and maintenance costs, and sets interference labels for each type of fault; identifies frequent information items based on several information items corresponding to each type of fault, determines push rules based on the co-occurrence frequency between frequent information items, and pushes them to the vehicle display terminal; calls up the operational data of the corresponding type of fault, and detects the type of fault based on the interference labels; and updates and pushes data synchronously based on the maintenance frequency. This invention, while assessing the impact of faults on vehicle operating efficiency in real time, promptly updates vehicle fault data and pushes it to the vehicle display terminal, facilitating relevant personnel to clearly understand the correlation between vehicle fault data, enabling rapid and accurate decision-making, and ensuring the safe and stable operation of the vehicle.

[0047] In particular, this invention considers the number of affected nodes and the duration of vehicle downtime caused by fault interference. In practice, the number of affected nodes reflects the scope of the fault in the subway network and the degree of interference to the overall operation of the subway. The duration of vehicle downtime directly reflects the degree of damage to the subway's operational efficiency caused by the fault. The maintenance cost reflects the direct costs of manpower and materials required to repair the fault, as well as the indirect costs such as potential operating revenue loss and passenger compensation. By evaluating the overall reliability of the subway system through the above three characteristics, this invention uses interference characteristics combined with maintenance costs to evaluate the operational interference characterization parameters of corresponding types of faults to characterize the severity of the impact of this type of fault on vehicle operation, providing data support for setting interference labels for subsequent types of faults. Under the premise of real-time evaluation of the impact of faults on vehicle operating efficiency, this invention updates the vehicle fault data in a timely manner and pushes it to the onboard display terminal, so that relevant personnel can clearly understand the correlation between vehicle fault data, facilitate quick and accurate decision-making, and ensure the safe and stable operation of the vehicles.

[0048] In particular, this invention identifies frequently occurring information items through operational data of various types of vehicle faults and determines the correlation between other remaining information items and these frequently occurring items to comprehensively determine the push display format. It selects frequently occurring and relatively important information items from a large number of fault-related information items within the operational data for focused push notifications, avoiding interference from irrelevant or low-frequency information items. This allows relevant personnel to quickly focus on key content closely related to the fault, improving the efficiency and relevance of information processing. Simultaneously, it clusters and integrates frequently occurring information items, using associated information groups to more comprehensively reflect the data involved in the fault and the interrelationships between these data. During the push display process, it determines the remaining... The semantic similarity between frequently accessed information items and the main titles of each push notification is used to determine the main title with the highest degree of relevance. This main title is then placed below the corresponding main title as a subtitle. This method constructs a clear information push structure, where the main title summarizes the main content and the subtitle provides more detailed information. This makes the information displayed on the vehicle's in-vehicle display layered and easy to understand and analyze fault conditions. Under the premise of real-time assessment of the impact of faults on vehicle operating efficiency, this invention updates vehicle fault data in a timely manner and pushes it to the in-vehicle display, enabling relevant personnel to clearly understand the correlation between vehicle fault data, facilitate quick and accurate decision-making, and ensure the safe and stable operation of the vehicle.

[0049] In particular, this invention accurately measures the actual impact and potential risks of various types of faults on vehicle operation by assessing the density of each type of fault. It considers the frequency of occurrence of each type of fault within a predetermined time period to reflect the frequency of fault occurrence, while the duration of adjacent occurrences reflects the length of time corresponding types of faults occur consecutively, further reflecting the density and frequency of fault occurrences. Simultaneously, it continuously monitors and analyzes fault types to reduce the probability of fault occurrence, minimize the interference of faults on normal subway operation and the impact on passenger travel, and improve the safety and stability of subway operation. Therefore, this invention uses fault density characterization values ​​to represent the density and frequency of the impact of corresponding types of faults on vehicles, providing data support for subsequent determination of whether corresponding types of faults have reached fault thresholds. This allows for adaptive adjustment of the maintenance frequency of components corresponding to the fault type. Under the premise of real-time assessment of the impact of faults on vehicle operating efficiency, this invention promptly updates vehicle fault data and pushes it to the onboard display, facilitating relevant personnel to clearly understand the correlation between vehicle fault data, enabling rapid and accurate decision-making, and ensuring the safe and stable operation of vehicles. Attached Figure Description

[0050] Figure 1 This is a functional block diagram of the vehicle multi-data association display method according to an embodiment of the invention;

[0051] Figure 2A logic decision diagram for setting interference labels for fault types in embodiments of the invention;

[0052] Figure 3 This is a logic diagram for detecting and analyzing type faults based on interference labels in an embodiment of the invention.

[0053] Figure 4 This is a logic diagram for determining whether a fault type has reached a fault threshold in an embodiment of the invention. Detailed Implementation

[0054] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0055] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0056] It should be noted that in the description of this invention, the terms "upper", "lower", "left", "right", "inner", "outer", etc., which indicate directions or positional relationships, are based on the directions or positional relationships shown in the accompanying drawings. This is only for the convenience of description and is not intended to indicate or imply that the device or element must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, it should not be construed as a limitation of this invention.

[0057] Furthermore, it should be noted that, in the description of this invention, unless otherwise explicitly specified and limited, the term "connection" should be interpreted broadly. For example, it can refer to a fixed connection, a detachable connection, or an integral connection; it can refer to a mechanical connection or an electrical connection. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0058] Please see Figure 1 As shown, Figure 1 This is a schematic diagram illustrating the steps of the vehicle multi-data association display method according to an embodiment of the present invention. The vehicle multi-data association display method according to an embodiment of the present invention includes:

[0059] Step S1: Obtain operational data for several types of faults corresponding to the vehicle to determine the interference characteristics of each type of fault. The interference characteristics include the number of affected nodes and the vehicle's stall duration.

[0060] Step S2: Based on the interference characteristics and maintenance costs, evaluate the operational interference characterization parameters of each type of fault to set the interference label for the type of fault;

[0061] Step S3: Based on several information items corresponding to the type of fault, identify frequent information items, determine push rules based on the co-occurrence frequency among the frequent information items, and push them to the vehicle display terminal.

[0062] Step S4: Call the operating data of the corresponding type of fault, and perform detection and analysis of the type of fault according to the interference label, including determining the occurrence frequency and adjacent occurrence duration of the type of fault within a predetermined time period, so as to evaluate the fault density characterization value of the corresponding type of fault, so as to determine whether the type of fault has reached the fault threshold, and adjust the maintenance frequency of the component corresponding to the type of fault according to the fault density characterization value.

[0063] Step S5: Update and push synchronously according to the maintenance frequency.

[0064] Specifically, there are no specific limitations on the method of obtaining runtime data. Relevant databases are pre-established to store runtime data, and runtime data is obtained by calling the relevant databases.

[0065] The operational data includes interference characteristics of several types of faults, corresponding information items, and the frequency of occurrence of several types of faults within a predetermined time period, as well as the duration of adjacent occurrences.

[0066] There are no restrictions on the type of database; it can be any existing technology, such as Oracle, which will not be elaborated further.

[0067] Specifically, in the operational data of the corresponding type of vehicle fault, the description of the vehicle fault includes basic fault information, fault description information, vehicle operating status data, vehicle component information, and maintenance record information. By extracting the keywords corresponding to each part of the information as information items, such as fault phenomenon, fault component name, vehicle number, maintenance measures, etc., this will not be elaborated further.

[0068] Specifically, the onboard display terminal refers to the driver's control console display screen installed in the driver's cab of a subway car.

[0069] It is understandable that for faults marked as severe interference, the number of affected nodes is large, the vehicle downtime is long, and the maintenance cost is high. In other words, the impact of this type of fault on vehicle operation is severe. Therefore, this type of fault should be detected and analyzed with a large number of data samples. In this embodiment, the predetermined time period is set to 1 month, and the frequency of occurrence of the corresponding type of fault and the duration of adjacent occurrences are analyzed within 1 month.

[0070] The term "line node" refers to several stations along the predetermined route traveled by the vehicle. The stations affected by the type of fault are designated as the affected nodes. The predetermined route is determined during the subway network planning stage and will not be elaborated further.

[0071] Specifically, the process of evaluating the operational interference characterization parameters of each type of fault based on the interference characteristics and maintenance costs includes,

[0072] The sum of the ratio of the number of affected nodes to the threshold of the number of affected nodes and the ratio of the vehicle stagnation time to the threshold of the vehicle stagnation time is used as the first operational interference feature.

[0073] The ratio of maintenance cost to maintenance cost threshold is used as the second operational interference feature;

[0074] The weighted sum of the first operational interference feature and the second operational interference feature is used as the operational interference characterization parameter.

[0075] Specifically, during actual vehicle operation, the disruption to normal operation caused by malfunctions can be directly reflected in the number of affected nodes and the duration of vehicle downtime. Therefore, in implementation, the interference characteristics are given priority. Thus, the first operational interference characteristic calculated based on the interference characteristics is assigned a slightly higher weight. Therefore, when performing weighted summation, the weight of the first operational interference characteristic is set to 0.6, and the weight of the second operational interference characteristic is set to 0.4.

[0076] In this embodiment, the purpose of setting thresholds for the number of affected nodes, vehicle downtime, and maintenance cost is to characterize situations where the severity of the impact of this type of fault on vehicle operation is high. By acquiring historical operational data of several instances of the same type of fault, and calling historical data on the number of affected nodes, vehicle downtime, and maintenance cost, the average number of affected nodes, the average vehicle downtime, and the average maintenance cost are calculated. Based on the purpose of setting the above three thresholds, the threshold for the number of affected nodes is determined as the product of the average number of affected nodes and the number deviation coefficient; the threshold for vehicle downtime is determined as the product of the average vehicle downtime and the duration deviation coefficient; and the threshold for maintenance cost is determined as the product of the average maintenance cost and the cost deviation coefficient. The number deviation coefficient is selected within the interval [1.2, 1.5], the duration deviation coefficient is selected within the interval [1.2, 1.25], and the cost deviation coefficient is selected within the interval [1.2, 1.3].

[0077] Specifically, this invention considers the number of affected nodes and the duration of vehicle downtime caused by a fault. In practice, the number of affected nodes reflects the scope of the fault's impact on the subway network and the degree of disruption to the overall subway operation. The duration of vehicle downtime directly reflects the extent to which the fault affects the subway's operational efficiency. For example, a longer downtime means a longer period of inoperability, leading to delays and even potential adjustments to timetables, disrupting normal operations. Maintenance costs reflect the direct costs of repairing the fault, such as manpower and resources, as well as the indirect costs, such as potential revenue losses and passenger compensation. The invention evaluates the subway system based on these three characteristics. The overall reliability of the system is crucial. For example, if a certain type of fault frequently leads to a large number of affected nodes, prolonged vehicle downtime, and high maintenance costs, then the maintenance efficiency for this type of fault needs to be optimized to ensure the reliability of vehicle components. Therefore, this invention assesses the operational interference characterization parameters of the corresponding type of fault by combining interference features with maintenance costs to characterize the severity of the impact of this type of fault on vehicle operation. This provides data support for subsequently setting interference labels for the type of fault. Under the premise of real-time assessment of the impact of faults on vehicle operating efficiency, this invention updates vehicle fault data in a timely manner and pushes it to the on-board display terminal, making it easier for relevant personnel to clearly understand the correlation between vehicle fault data, facilitate quick and accurate decision-making, and ensure the safe and stable operation of the vehicle.

[0078] Specifically, please refer to Figure 2 As shown, this is a logic decision diagram for setting interference labels for fault types in an embodiment of the present invention. Setting the interference labels for the fault types includes,

[0079] If the operational interference characterization parameter of any type of fault is greater than or equal to the operational interference characterization parameter threshold, then a severe interference label is set for that type of fault.

[0080] The threshold values ​​for the interferometric characterization parameters are selected within the range [1.68, 1.74].

[0081] Specifically, the process of identifying frequent information items includes,

[0082] Determine the word frequency of several information items;

[0083] Information items whose word frequency is greater than or equal to the word frequency threshold are identified as frequent information items.

[0084] In this embodiment, the purpose of setting a word frequency threshold is to characterize the situation where information items appear frequently in the description of vehicle faults. By obtaining relevant data on the description of vehicle faults in the operating data corresponding to several occurrences of the same type of fault, calling word frequency data, solving for the word frequency mean, and based on the purpose of setting a word frequency threshold, the word frequency threshold is determined as the product of the word frequency mean and the word frequency deviation coefficient, wherein the word frequency deviation coefficient is selected in the interval [1.15, 1.2].

[0085] The ratio of the number of any information item to the total number of information items is taken as the word frequency of the information item.

[0086] Specifically, the process of determining push rules based on the co-occurrence frequency among the frequently occurring information items includes,

[0087] Cluster the frequent information items that meet the frequent co-occurrence criteria;

[0088] The frequently occurring information items after clustering are used as the main title of the push notification;

[0089] Obtain the semantic similarity between the remaining frequent information items (excluding the main push title) and each of the main push titles;

[0090] Sort the semantic similarities in descending order;

[0091] The remaining frequently occurring information items are placed under the main title of the push notification corresponding to the first item in the descending sort as the push notification subtitle;

[0092] The frequent co-occurrence criteria include a co-occurrence frequency greater than or equal to a co-occurrence frequency threshold.

[0093] Specifically, there are no restrictions on how word frequencies are determined. They can be determined using statistical software, such as Excel, which uses pivot tables to count the number of times information items appear, and then determines the word frequency corresponding to the information items. Of course, other methods can also be used, which will not be elaborated here.

[0094] Specifically, there is no limitation on how to determine the co-occurrence frequency between frequent information items. The co-occurrence frequency of frequent information items can be calculated by writing SQL query statements. Of course, other methods can also be used to determine it, which will not be elaborated here.

[0095] Specifically, there is no limitation on how the semantic similarity between the remaining frequent information items and the main title of the push notification is determined. It can be determined based on deep learning methods, such as word vector models, such as Word2Vec and GloVe. The remaining frequent information items are mapped into a low-dimensional vector space, and the semantic similarity between the frequent information items and the main title of the push notification is determined by calculating the distance between word vectors, such as cosine similarity or Euclidean distance. This will not be elaborated further.

[0096] In this embodiment, the purpose of setting the co-occurrence frequency threshold is to characterize the situation where the degree of correlation between the remaining frequent information items and the main title of the push is high. By obtaining the operation history data of several times the same type of failure, calling the co-occurrence frequency history data, solving the mean co-occurrence frequency, and based on the purpose of setting the co-occurrence frequency threshold, the co-occurrence frequency threshold is determined as the product of the mean co-occurrence frequency and the frequency offset coefficient, wherein the frequency offset coefficient is selected in the interval [1.1, 1.15].

[0097] Specifically, this invention identifies frequently occurring information items through operational data of various types of vehicle faults, and determines the relationship between other remaining information items and frequently occurring information items to comprehensively determine the push display format. From a large number of fault-related information items in the operational data, it selects frequently occurring and relatively important information items for focused push, which can avoid interference from irrelevant or low-frequency information items, enabling relevant personnel to quickly focus on key content closely related to the fault, improving the efficiency and relevance of information processing. At the same time, it clusters and integrates frequently occurring information items, and through the form of associated information groups, it more comprehensively reflects the data involved in the fault and the influence and relationship between the data.

[0098] During the push notification process, the semantic similarity between the remaining frequently occurring information items and each push notification title is determined. The push notification title with the highest degree of relevance is then placed below the corresponding title as the push notification subtitle. This method constructs a clear information push structure, where the push notification title summarizes the main content, and the push notification subtitle provides more detailed related information. This ensures that the information displayed on the vehicle's in-vehicle display is clearly hierarchical, facilitating understanding and analysis of fault conditions. Under the premise of real-time assessment of the impact of faults on vehicle operating efficiency, this invention updates vehicle fault data in a timely manner and pushes it to the in-vehicle display, enabling relevant personnel to clearly understand the correlation between vehicle fault data, facilitate quick and accurate decision-making, and ensure the safe and stable operation of the vehicle.

[0099] Specifically, please refer to Figure 3 As shown, this is a logic decision diagram for detecting and analyzing type faults based on interference tags according to an embodiment of the present invention. The detection and analysis of the type faults based on the interference tags includes...

[0100] If any type of fault with a severely interfering label is found, then the fault type will be detected and analyzed.

[0101] Specifically, the process of evaluating the fault density representation value for the corresponding type of fault includes,

[0102] The ratio of occurrence frequency to occurrence frequency threshold is used as the first fault density feature;

[0103] The ratio of the adjacency occurrence duration threshold to the adjacency occurrence duration is used as the second fault density feature;

[0104] The sum of the first fault-dense feature and the second fault-dense feature is taken as the fault-dense characterization value.

[0105] In this embodiment, the purpose of setting the occurrence frequency threshold and the adjacent occurrence duration threshold is to characterize the situation where the impact of the corresponding type of fault on vehicle operation is relatively dense and frequent. By acquiring the operation history data of several occurrences of the same type of fault, calling the occurrence frequency history data and the adjacent occurrence duration history data, the average occurrence frequency and the average adjacent occurrence duration are calculated. Based on the purpose of setting the above two thresholds, the occurrence frequency threshold is determined as the product of the occurrence frequency average and the frequency deviation coefficient, and the adjacent occurrence duration threshold is determined as the product of the adjacent occurrence duration average and the duration offset coefficient. The frequency deviation coefficient is selected in the interval [1.2, 1.5], and the duration offset coefficient is selected in the interval [0.85, 0.94].

[0106] Specifically, this invention accurately measures the actual impact and potential risks of various types of faults on vehicle operation by assessing the density of each fault type. It considers the frequency of occurrence of each type of fault within a predetermined time period to reflect the frequency of fault occurrence. The duration of adjacent occurrences reflects the length of time that corresponding types of faults occur consecutively, further reflecting the density and frequency of fault occurrences. Simultaneously, it continuously monitors and analyzes fault types. For example, increasing the maintenance frequency of components designed for fault types with high frequency of occurrence can more promptly detect and address potential problems, reduce the likelihood of fault occurrence, minimize the interference of faults on normal subway operation and the impact on passenger travel, and improve the safety and stability of subway operation. Therefore, this invention uses fault density characterization values ​​to represent the density and frequency of the impact of corresponding types of faults on the vehicle, providing data support for subsequent determination of whether the corresponding type of fault has reached the fault threshold. This allows for adaptive adjustment of the maintenance frequency of components corresponding to the fault type. Under the premise of real-time assessment of the impact of faults on vehicle operating efficiency, this invention promptly updates vehicle fault data and pushes it to the onboard display, facilitating relevant personnel to clearly understand the correlation between vehicle fault data, enabling rapid and accurate decision-making, and ensuring the safe and stable operation of the vehicle.

[0107] Specifically, please refer to Figure 4 As shown, this is a logic diagram for determining whether a fault type has reached a fault threshold according to an embodiment of the present invention. Determining whether the fault type has reached a fault threshold includes:

[0108] If the fault density representation value of any type of fault is greater than or equal to the fault density representation threshold, then the fault of that type is determined to have reached the fault threshold.

[0109] The threshold for dense fault characterization is selected within the interval [2.14, 2.21].

[0110] Specifically, adjusting the maintenance frequency of components corresponding to the type of fault based on the fault density characteristic value includes,

[0111] The maintenance frequency is positively correlated with the fault density characteristic value.

[0112] In this embodiment, optionally,

[0113] The fault density representation value is compared with the preset first fault density representation comparison threshold and the second fault density representation comparison threshold.

[0114] When the fault density characterization value is greater than the second fault density characterization comparison threshold, the maintenance frequency is determined to be the first maintenance frequency. The first maintenance frequency is set to be 1.8 times the base maintenance frequency.

[0115] When the fault density characterization value is greater than or equal to the first fault density characterization comparison threshold and less than or equal to the second fault density characterization comparison threshold, the maintenance frequency is determined to be the second maintenance frequency. The second maintenance frequency is set to be 1.6 times the base maintenance frequency.

[0116] When the fault density characterization value is less than the first fault density characterization comparison threshold, the maintenance frequency is determined to be the third maintenance frequency. The third maintenance frequency is set to be 1.4 times the base maintenance frequency.

[0117] Among them, the first fault dense characterization comparison threshold is 1.1 times the fault dense characterization threshold, and the second fault dense characterization comparison threshold is 1.3 times the fault dense characterization threshold.

[0118] The baseline maintenance frequency can be determined by those skilled in the art based on the vehicle's mileage, operating time, and relevant standards, and will not be elaborated further here.

[0119] Specifically, a system for displaying multi-data association of vehicles is also provided, characterized in that it includes:

[0120] The data acquisition module is used to acquire operational data of several types of faults corresponding to the vehicle, so as to determine the interference characteristics of each type of fault, including the number of affected nodes and the vehicle's downtime.

[0121] An interference assessment module, connected to the data acquisition module, is used to assess the operational interference characterization parameters of each type of fault based on the interference characteristics and maintenance costs, so as to set the interference labels for the type of fault.

[0122] The associated push module is connected to the data acquisition module and is used to identify frequent information items based on several information items corresponding to the type of fault, determine push rules based on the co-occurrence frequency between the frequent information items, and push them to the vehicle display terminal.

[0123] The operation analysis module, which is connected to the interference evaluation module, is used to call the operation data of the corresponding type of fault, and to detect and analyze the type of fault according to the interference label. This includes determining the occurrence frequency and adjacent occurrence duration of the type of fault within a predetermined time period, evaluating the fault density characterization value of the corresponding type of fault, determining whether the type of fault has reached the fault threshold, and adjusting the maintenance frequency of the component corresponding to the type of fault according to the fault density characterization value.

[0124] The push update module is connected to the operation analysis module and is used to push updates synchronously according to the maintenance frequency.

[0125] Specifically, there are no restrictions on the specific structure of the data acquisition module, interference assessment module, correlation push module, operation analysis module, push update module, and fault early warning module. Each module or its units can be composed of logical components or combinations of logical components. Logical components include field-programmable processors, computers, or microprocessors in computers.

[0126] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

Claims

1. A vehicle multi-data correlation display method characterized by, The method comprises the following steps: acquiring operation data of several types of faults corresponding to a vehicle to determine interference features of each type of fault, the interference features comprising the number of affected nodes and the length of time when the vehicle is stalled; evaluating operation interference representation parameters of each type of fault according to the interference features and repair costs to set an interference label of the type of fault; based on several information items corresponding to the type of fault, identifying frequent information items, determining a push rule according to the co-occurrence frequency between the frequent information items, and pushing to a vehicle-mounted display terminal; calling operation data of the corresponding type of fault, and detecting and analyzing the type of fault according to the interference label, comprising determining the occurrence frequency and the length of time when adjacent faults occur of the type of fault within a predetermined time period to evaluate a fault density representation value of the corresponding type of fault, to determine whether the type of fault reaches a fault threshold, and adjusting the repair frequency of the component corresponding to the type of fault according to the fault density representation value; synchronously updating and pushing according to the repair frequency.

2. The vehicle multi-data correlation presentation method according to claim 1, characterized by, The process of evaluating operation interference representation parameters of each type of fault according to the interference features and repair costs comprises the following steps: taking the sum of the ratio of the number of affected nodes to the number of affected node threshold and the ratio of the length of time when the vehicle is stalled to the length of time when the vehicle is stalled threshold as a first operation interference feature; taking the ratio of the repair cost to the repair cost threshold as a second operation interference feature; weighting and summing the first operation interference feature and the second operation interference feature as the operation interference representation parameter.

3. The vehicle multi-data correlation presentation method according to claim 2, characterized by, Setting the interference label of the type of fault comprises the following steps: if the operation interference representation parameter of any type of fault is greater than or equal to the operation interference representation parameter threshold, setting a serious interference label for the type of fault. The process of identifying frequent information items comprises the following steps:

4. The vehicle multi-data correlation presentation method according to claim 1, characterized by, determining the word frequency of several information items; identifying information items with a word frequency greater than or equal to a word frequency threshold as the frequent information items. The process of determining a push rule according to the co-occurrence frequency between the frequent information items comprises the following steps: clustering frequent information items that meet the frequent co-occurrence standard; 5. The vehicle multi-data correlation presentation method according to claim 4, characterized by, taking the clustered frequent information items as the push main title; acquiring the semantic similarity between the remaining frequent information items and each push main title; descendingly sorting the semantic similarity; placing the remaining frequent information items under the corresponding push main title at the first position of the descendingly sorted push main title as the push subtitle; wherein the frequent co-occurrence standard comprises a co-occurrence frequency greater than or equal to a co-occurrence frequency threshold. Detecting and analyzing the type of fault according to the interference label comprises the following steps: if there is any type of fault with a serious interference label, detecting and analyzing the type of fault.

6. The vehicle multi-data correlation presentation method according to claim 3, characterized by, The process of evaluating the fault density representation value of the corresponding type of fault comprises the following steps: taking the ratio of the occurrence frequency to the occurrence frequency threshold as a first fault density feature; 7. The vehicle multi-data correlation presentation method according to claim 1, characterized by, taking the ratio of the length of time when adjacent faults occur to the length of time when adjacent faults occur threshold as a second fault density feature; taking the sum of the first fault density feature and the second fault density feature as the fault density representation value. Determining whether the type of fault reaches a fault threshold comprises the following steps: ​ 8. The vehicle multi-data correlation presentation method according to claim 7, characterized by, ​ If the fault-intensive representation value of any type of fault is greater than or equal to the fault-intensive representation threshold, the type of fault is determined to reach the fault threshold.

9. The vehicle multi-data correlation presentation method according to claim 1, characterized by, The maintenance frequency of the component corresponding to the type of fault is adjusted according to the fault-intensive representation value, Comprising, The maintenance frequency is positively correlated with the fault-intensive representation value.

10. A system for applying the method of multi-data association presentation of a vehicle according to any one of claims 1 to 9, characterized in that, Comprising, A data acquisition module is configured to acquire operation data of a plurality of types of faults corresponding to a vehicle, to determine an interference feature of each type of fault, wherein the interference feature comprises an affected node quantity and a vehicle stall duration; An interference evaluation module is connected with the data acquisition module and configured to evaluate an operation interference representation parameter of each type of fault according to the interference feature and maintenance cost, to set an interference label of the type of fault; An association pushing module is connected with the data acquisition module and configured to identify frequent information items based on a plurality of information items corresponding to the type of fault, to determine a pushing rule according to a co-occurrence frequency between the frequent information items, and to push to a vehicle-mounted display end; An operation analysis module is connected with the interference evaluation module and configured to call operation data of the corresponding type of fault, to perform detection analysis on the type of fault according to the interference label, Comprising, determining an occurrence frequency and an adjacent occurrence duration of the type of fault within a predetermined time period, to evaluate a fault-intensive representation value of the corresponding type of fault, to determine whether the type of fault reaches a fault threshold, and to adjust the maintenance frequency of the component corresponding to the type of fault according to the fault-intensive representation value; A pushing update module is connected with the operation analysis module and configured to synchronously perform update pushing according to the maintenance frequency.

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