Railway knowledge graph data updating frequency evaluation method based on life cycle prediction

Abstract

The invention relates to the technical field of knowledge graph management, in particular to a railway knowledge graph data updating frequency evaluation method based on life cycle prediction. The method comprises the steps of obtaining a target importance degree value of each monitoring data sequence subset corresponding to each railway facility category according to each monitoring data sub-sequence in a monitoring data sub-sequence set, obtaining a weight value corresponding to each monitoring data sequence in the monitoring data sequence set corresponding to each railway facility according to the target importance degree value of each monitoring data sequence subset; and according to the weight value corresponding to each monitoring data sequence, obtaining a predicted residual life corresponding to each railway facility, and according to the predicted residual life corresponding to each railway facility, obtaining a future update frequency corresponding to each railway facility. According to the method, resource consumption during updating of the railway knowledge graph can be reduced, and high efficiency and accuracy of railway management can be guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of knowledge graph management, and in particular to a method for evaluating the update frequency of railway knowledge graph data based on life cycle prediction. Background Art

[0002] The railway knowledge graph is an integrated information management system, also known as the railway infrastructure graph. It combines various data on railway facilities, such as data on railway facility performance, data on railway facility maintenance, and data on railway facility failures. In addition, the railway knowledge graph makes railway management more efficient and accurate by integrating various data on railway facilities into a structured knowledge system or by associating various data on railway facilities.

[0003] Because the data on railway facility performance, railway facility maintenance, and railway facility failures will continue to increase over time, after the railway infrastructure knowledge graph is constructed, the multi-dimensional data of each railway facility in the railway knowledge graph needs to be updated to ensure the efficiency and accuracy of real-time railway management based on the railway knowledge graph, and the process of updating the multi-dimensional data of each railway facility in the railway knowledge graph belongs to the management process of the railway knowledge graph; and currently, when updating the multi-dimensional data of each railway facility in the railway knowledge graph, the update frequency of each railway facility is usually set to an empirical value, but in the update process, if a higher update frequency is set for some railway facilities with a longer remaining service life, and a lower update frequency is set for some railway facilities with a shorter remaining service life, not only will a large amount of various resources, including computing resources, human resources, time resources, etc., be wasted or consumed, but also the efficiency and accuracy of railway management will be reduced. Therefore, in the process of updating the multi-dimensional data of each railway facility in the railway knowledge graph, how to obtain the update time or update frequency of various railway facilities is an urgent problem to be solved. Summary of the invention

[0004] In order to solve the above problems, the present invention provides a method for evaluating the update frequency of railway knowledge graph data based on life cycle prediction. The technical solution adopted is as follows: An embodiment of the present invention provides a method for evaluating the update frequency of railway knowledge graph data based on life cycle prediction, comprising the following steps: Obtain a monitoring data sequence set corresponding to each railway facility in each railway facility category in the railway facility category set corresponding to the railway knowledge graph, a monitoring time period corresponding to each monitoring data sequence in the monitoring data sequence set, and a tag value corresponding to each monitoring data sequence; According to the tag values ​​corresponding to the monitoring data sequences, obtaining the monitoring data sequence subsets corresponding to the railway facility categories; according to the monitoring data sequences in the monitoring data sequence subsets and the monitoring time periods corresponding to the monitoring data sequences, obtaining the monitoring data subsequence sets corresponding to the monitoring data sequences in the monitoring data sequence subsets; According to each monitoring data subsequence in the monitoring data subsequence set, a target importance value of each monitoring data sequence subset corresponding to each railway facility category is obtained; Obtaining, according to the target importance values ​​of each monitoring data sequence subset, a weight value corresponding to each monitoring data sequence in the monitoring data sequence set corresponding to each railway facility; According to the weight values ​​corresponding to the monitoring data sequences, the predicted remaining life of each railway facility is obtained, and according to the predicted remaining life of each railway facility, the future update frequency of each railway facility is obtained.

[0005] Beneficial effects: The present invention obtains each monitoring data sequence subset corresponding to each railway facility category according to the tag value corresponding to each acquired monitoring data sequence, and obtains a monitoring data subsequence set corresponding to each monitoring data sequence in each monitoring data sequence subset according to each monitoring data sequence in the acquired monitoring data sequence subset and the monitoring time period corresponding to each monitoring data sequence; then, according to each monitoring data subsequence in the monitoring data subsequence set, the target importance value of each monitoring data sequence subset corresponding to each railway facility category is obtained, and according to the target importance value of each monitoring data sequence subset, the weight value corresponding to each monitoring data sequence in the monitoring data sequence set corresponding to each railway facility is obtained; finally, according to the weight value corresponding to each monitoring data sequence, the predicted remaining life corresponding to each railway facility is obtained, and according to the predicted remaining life corresponding to each railway facility, the future update frequency corresponding to each railway facility is obtained. The present invention can more reliably predict the remaining life of each railway facility by obtaining the weight value corresponding to each monitoring data sequence in the monitoring data sequence set corresponding to each railway facility through the target importance value of each monitoring data sequence subset, that is, can more reliably obtain the predicted remaining life of each railway facility, and the future update frequency of each railway facility obtained based on the predicted remaining life of each railway facility can not only reduce the resource consumption when updating the railway knowledge graph, but also ensure the efficiency and accuracy of railway management. BRIEF DESCRIPTION OF THE DRAWINGS

[0006] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0007] Figure 1 The present invention is a flow chart of a method for evaluating the update frequency of railway knowledge graph data based on life cycle prediction. DETAILED DESCRIPTION

[0008] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field belong to the scope of protection of the embodiments of the present invention.

[0009] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0010] This embodiment provides a method for evaluating the update frequency of railway knowledge graph data based on life cycle prediction, which is described in detail as follows: like Figure 1 As shown, the railway knowledge graph data update frequency evaluation method based on life cycle prediction includes the following steps: Step S001, obtain a set of monitoring data sequences corresponding to each railway facility in each railway facility category in a set of railway facility categories corresponding to a railway knowledge graph, a monitoring time period corresponding to each monitoring data sequence in the monitoring data sequence set, and a label value corresponding to each monitoring data sequence.

[0011] The main purpose of this embodiment is to determine the update time or update frequency of each railway facility according to the predicted remaining life of each railway facility in the process of updating the multi-dimensional data of each railway facility in the railway knowledge graph, so as to reduce the resource consumption when updating the railway knowledge graph while ensuring or improving the efficiency and accuracy of real-time railway management; in addition, the railway knowledge graph in this embodiment is a knowledge graph that has been constructed, that is, this embodiment does not involve the construction process of the railway knowledge graph, but is aimed at the update management process of the railway knowledge graph after the construction of the railway knowledge graph, that is, the process of determining the update frequency of the multi-dimensional data of each railway facility in the railway knowledge graph is the update management process of the railway knowledge graph.

[0012] In addition, for the convenience of analysis, this embodiment will subsequently take the update process of any railway knowledge graph that has been constructed as an example for analysis, that is, the railway knowledge graphs that appear subsequently in this embodiment all refer to the same railway knowledge graph, and all railway facilities that appear subsequently belong to the railway facilities in the same railway knowledge graph; therefore, this embodiment first records the set constructed by all railway facilities in the railway knowledge graph as a railway facility set, and there are railway facilities of repeated types in the railway facility set, for example, there are multiple railway signal lights in the railway facility set; in addition, the railway facilities in this embodiment are all facilities that can be monitored.

[0013] Then, the types of all railway facilities appearing in the railway facility set are counted and recorded as the railway facility categories corresponding to the railway facility set, and the set constructed by all railway facility categories corresponding to the railway facility set is recorded as the railway facility category set corresponding to the railway knowledge graph, and the types of all railway facilities in the railway facility category are the same. For example, all railway signal lights in the railway facility set can form a railway facility category.

[0014] This embodiment next needs to obtain the current monitoring time period corresponding to each railway facility in the railway facility set, and the current monitoring time period corresponding to each railway facility refers to the time period formed from the beginning of the corresponding railway facility being put into use to the current moment; then, in the current monitoring time period corresponding to each railway facility, all monitoring data corresponding to each railway facility and the collection time corresponding to all monitoring data are collected, and among all the monitoring data corresponding to the railway facilities, there will be monitoring data with repeated collection time but inconsistent monitoring data types; therefore, in the current monitoring time period corresponding to each railway facility, all monitoring data corresponding to each railway facility and the collection time corresponding to all monitoring data can be obtained.

[0015] For any railway facility a0 in any railway facility category B0 in the railway facility category set: a set constructed by all monitoring data corresponding to the railway facility a0 obtained in the current monitoring time period corresponding to the railway facility a0 is recorded as the monitoring data set corresponding to the railway facility a0; and the types of all monitoring data in the monitoring data set corresponding to the railway facility a0 are statistically obtained, and are all recorded as the monitoring data type corresponding to the railway facility a0, that is, the monitoring data type corresponding to the railway facility a0 monitored and collected is multidimensional; then in the monitoring data set corresponding to the railway facility a0, a sequence constructed by monitoring data of the same monitoring data type is recorded as the monitoring data sequence corresponding to the railway facility a0, and the number of monitoring data sequences corresponding to the railway facility a0 is the same as the number of types of the monitoring data type corresponding to the railway facility a0, that is, one monitoring data type can obtain one monitoring data sequence, and the monitoring data in the monitoring data sequence is arranged in the order of collection time; then a set constructed by all monitoring data sequences corresponding to the railway facility a0 is recorded as the monitoring data sequence set corresponding to the railway facility a0; in addition, this embodiment uses the current monitoring time period corresponding to the railway facility a0 as the monitoring time period corresponding to each monitoring data sequence in the monitoring data sequence set corresponding to the railway facility a0.

[0016] Therefore, according to the above method of obtaining the monitoring data sequence set corresponding to the railway facility a0, the monitoring data sequence set corresponding to each railway facility can be obtained.

[0017] In the present embodiment, for any railway facility category, the number of monitoring data sequences in the monitoring data sequence set corresponding to all railway facilities in the railway facility category is the same, and the types of monitoring data represented by the monitoring data sequences located at the same position are consistent, that is, the types of monitoring data in the monitoring data sequences located at the same position are the same. For example, if the railway facility category refers to traffic lights, and the type of monitoring data in the x-th monitoring data sequence in the monitoring data sequence set corresponding to any railway facility in the railway facility category refers to the luminous intensity of the railway facility, then the type of monitoring data in the x-th monitoring data sequence in the monitoring data sequence set corresponding to the remaining railway facilities in the railway facility category all refers to the luminous intensity of the railway facility.

[0018] In addition, the monitoring data types in this embodiment include but are not limited to the performance data, fault record data, and maintenance record data of railway facilities; and the performance data of facilities mainly refers to the technical performance indicators shown by the facilities during operation or use. These indicators reflect the efficiency and effect of the facilities during use, and different types of railway facilities can monitor and collect different performance data. For example, the luminous intensity and working voltage of signal lights on the railway belong to the performance data of signal lights, and the gauge, plane and elevation deviation of the tracks on the railway belong to the performance data of the tracks. And this embodiment requires that the monitoring data types monitored and collected by all railway facilities in the same railway facility category are the same. For example, the monitoring data types corresponding to all signal lights on the railway include luminous intensity, working voltage, fault record data, and maintenance record data.

[0019] In this embodiment, the performance data of railway facilities are usually collected based on detection equipment, and the collection frequency is usually set to an empirical value, while the fault record data and maintenance record data are usually obtained manually.

[0020] In addition, based on the above description, it can be known that the monitoring data types obtained in this embodiment also include the fault record data and maintenance record data of the railway facilities, and these record data are usually in the form of coding. For example, for any railway facility, all fault types and all maintenance types existing in the railway facility are obtained, and then all fault types existing in the railway facility are coded, and all maintenance types existing in the railway facility are also coded. If there are three types of all fault types existing in the railway facility, then the codes of these three fault types existing in the railway facility are 1, 2, and 3 respectively. Similarly, if there are four types of all maintenance types existing in the railway facility, then the codes of these three fault types existing in the railway facility are 1, 2, 3, and 4 respectively; if a fault type coded as 1 appears at a certain moment in the monitoring time period corresponding to the railway facility, then the fault record data at that moment is 1, and similarly, if maintenance is performed at a certain moment in the monitoring time period corresponding to the railway facility and the maintenance type is coded as 2, then the maintenance record data at that moment is 2.

[0021] Therefore, based on the above process, this embodiment can obtain the monitoring data sequence set corresponding to each railway facility in each railway facility category and the monitoring time period corresponding to each monitoring data sequence in the monitoring data sequence set. Next, this embodiment will obtain the tag value corresponding to each monitoring data sequence, and the specific acquisition process is: For railway facility category B0: the monitoring data sequence sets corresponding to each railway facility in railway facility category B0 are all recorded as the feature sequence sets corresponding to the railway facility category B0, the mark values ​​corresponding to the c-th monitoring data sequence in each feature sequence set corresponding to railway facility category B0 are all recorded as c, and the monitoring data types in the c-th monitoring data sequence in each feature sequence set are the same; that is, for all feature sequence sets corresponding to railway facility category B0, the mark values ​​of monitoring data sequences at the same position in all feature sequence sets are the same, and the types of monitoring data in all monitoring data sequences with the same mark values ​​are the same, that is, the monitoring data in the monitoring data sequences with the same mark values ​​belong to data of the same dimension.

[0022] At this point, based on the above process, this embodiment can obtain the monitoring data sequence set corresponding to each railway facility in each railway facility category in the railway facility category set corresponding to the railway knowledge graph, the monitoring time period corresponding to each monitoring data sequence in the monitoring data sequence set, and the label value corresponding to each monitoring data sequence.

[0023] Step S002, based on the tag value corresponding to each monitoring data sequence, obtain each monitoring data sequence subset corresponding to each railway facility category; based on each monitoring data sequence in the monitoring data sequence subset and the monitoring time period corresponding to each monitoring data sequence, obtain the monitoring data subsequence set corresponding to each monitoring data sequence in the monitoring data sequence subset; based on each monitoring data subsequence in the monitoring data subsequence set, obtain the target importance value of each monitoring data sequence subset corresponding to each railway facility category; based on the target importance value of each monitoring data sequence subset, obtain the weight value corresponding to each monitoring data sequence in the monitoring data sequence set corresponding to each railway facility.

[0024] Since the update frequency of each railway facility is usually set as an empirical value in the process of updating the multi-dimensional data of each railway facility in the railway knowledge graph, if a higher update frequency is set for some railway facilities with a longer remaining service life and a lower update frequency is set for some railway facilities with a shorter remaining service life, it will not only waste or consume a large amount of various resources, including computing resources, human resources, time resources, etc., but also reduce the efficiency and accuracy of real-time railway management. Because in order to reduce resource consumption during updating and to ensure the efficiency and accuracy of railway management, this embodiment will predict the remaining service life of each railway facility based on the monitoring data sequence set corresponding to each railway facility in each railway facility category obtained above, that is, determine the predicted remaining service life of each railway facility, and determine the next update time or future update frequency of each railway facility based on the predicted remaining service life of each railway facility. The next update time or future update frequency of each railway facility determined based on the predicted remaining service life of each railway facility can not only reduce the resource consumption when updating the railway knowledge graph, but also ensure the efficiency and accuracy of railway management.

[0025] Because the monitoring data corresponding to each railway facility in the present implementation is multidimensional, when subsequently predicting the remaining life of each railway facility, the remaining life is usually predicted based on all the monitoring data sequences corresponding to the railway facilities, and currently, the remaining life of each railway facility is usually predicted based on a weighted linear regression remaining life prediction model. In addition, in the process of training the weighted linear regression remaining life prediction model, the weight values ​​of all monitoring data sequences are usually set to the same, but the importance or influence of monitoring data of different dimensions on the remaining life prediction is different, that is, the importance or influence of monitoring data sequences of different monitoring data types on the remaining life prediction is different. Therefore, in the process of training the weighted linear regression remaining life prediction model, if all the monitoring data sequences involved in the training are The weight values ​​of the trained monitoring data sequences are all the same, which may lead to the monitoring data information corresponding to the monitoring data types with greater importance or influence on the remaining life prediction being ignored, thereby resulting in poor performance or generalization ability of the trained model. Therefore, in order to further ensure the accuracy of the subsequent remaining life prediction of railway facilities, this embodiment will analyze the importance or influence of monitoring data sequences of different monitoring data types on the remaining life prediction to obtain the weight value corresponding to each monitoring data sequence in the monitoring data sequence set corresponding to each railway facility. Subsequently, the weighted linear regression remaining life prediction model will be trained based on the weight value and the monitoring data sequence. Therefore, the specific process of obtaining the weight value corresponding to each monitoring data sequence in the monitoring data sequence set corresponding to each railway facility in this embodiment is as follows: In this embodiment, it is necessary to first obtain each monitoring data sequence subset corresponding to each railway facility category according to the tag value corresponding to each monitoring data sequence in the monitoring data sequence set corresponding to each railway facility in each railway facility category, and each monitoring data sequence subset is obtained to facilitate the subsequent acquisition of the target importance value of the monitoring data sequence subset. The specific acquisition process is as follows: For railway facility category B0 in the railway facility category set: The new set constructed from the set of monitoring data sequences corresponding to all railway facilities in railway facility category B0 is recorded as the comprehensive monitoring data sequence set corresponding to railway facility category B0; the sets constructed from all monitoring data sequences with the same tag value in the comprehensive monitoring data sequence set are all recorded as monitoring data sequence subsets corresponding to railway facility category B0, and the tag values ​​of all monitoring data sequences in any monitoring data sequence subset corresponding to railway facility category B0 are the same, and the number of monitoring data sequence subsets corresponding to railway facility category B0 is the same as the number of types of tag values ​​in the tag values ​​corresponding to all monitoring data sequences in the comprehensive monitoring data sequence set.

[0026] Therefore, through the above process, each monitoring data sequence subset corresponding to each railway facility category can be obtained.

[0027] Next, according to each monitoring data sequence in each monitoring data sequence subset corresponding to each railway facility category and the monitoring time period corresponding to each monitoring data sequence, a monitoring data subsequence set corresponding to each monitoring data sequence in each monitoring data sequence subset corresponding to each railway facility category is obtained, and the monitoring data subsequence set is an important basis for subsequently obtaining the target importance value of each monitoring data sequence subset. Therefore, the specific method for obtaining the monitoring data subsequence set corresponding to each monitoring data sequence is: A preset time length is set, and in specific applications, the implementer needs to set the preset time length according to actual conditions. Since the monitoring data types in this embodiment include fault record data and maintenance record data, and the fault record data and maintenance record data are usually sparse, this embodiment requires that the preset time length be set longer in order to facilitate more reliable acquisition of the target importance value in the subsequent period, that is, the value of the preset time length is larger. For example, the value of the preset time length can be set to 1 month in this embodiment.

[0028] Then, the monitoring time periods corresponding to the monitoring data sequences are divided by the preset time lengths, and the monitoring data subsequence sets corresponding to each monitoring data sequence are obtained based on the division results. For ease of understanding, this embodiment will be described below by taking the acquisition process of the monitoring data subsequence set corresponding to the monitoring data sequence A0 in the monitoring data sequence subset C0 corresponding to the railway facility category B0 as an example. Therefore, the specific method for acquiring the monitoring data subsequence set corresponding to the monitoring data sequence A0 is as follows: For the monitoring time period T0 corresponding to the monitoring data sequence A0, first, starting from the start time of the monitoring time period T0, the monitoring time period T0 is evenly divided using the preset time length to obtain all monitoring sub-time periods corresponding to the monitoring time period T0. For any monitoring sub-time period t0 corresponding to the monitoring time period T0: In the monitoring data sequence A0, the sequence constructed by all monitoring data whose collection time is in the monitoring sub-time period t0 is recorded as the monitoring data sub-sequence corresponding to the monitoring sub-time period t0.

[0029] In addition, when the monitoring time period T0 is evenly divided using the preset time length, it may happen that the time length of the sub-time period including the last moment in the monitoring time period T0 is less than the preset time length. In this embodiment, the sub-time period with a time length less than the preset time length is used as the monitoring sub-time period corresponding to the monitoring time period T0.

[0030] Therefore, based on the above process, the monitoring data subsequences corresponding to all monitoring sub-time periods corresponding to the monitoring time period T0 can be obtained, and in this embodiment, the subsequence set constructed by the monitoring data subsequences corresponding to all monitoring sub-time periods corresponding to the monitoring time period T0 is recorded as the monitoring data subsequence set corresponding to the monitoring data sequence A0.

[0031] In this embodiment, the monitoring data subsequence sets corresponding to all monitoring data sequences can be obtained according to the above method for obtaining the monitoring data subsequence set corresponding to the monitoring data sequence A0. After obtaining the monitoring data subsequence set corresponding to the monitoring data sequence, this embodiment will analyze each monitoring data subsequence in the monitoring data subsequence set to determine the target importance value of each monitoring data sequence subset corresponding to each railway facility category, and the target importance value is an important indicator in the subsequent determination of the weight value corresponding to each monitoring data sequence in the monitoring data sequence set corresponding to each railway facility. Therefore, the specific process of obtaining the target importance value of each monitoring data sequence subset in this embodiment is as follows: For the monitoring data sequence subset C0 corresponding to the railway facility category B0: First, any two monitoring data sequences in the monitoring data sequence subset C0 are randomly arranged and combined without repetition to obtain all monitoring data sequence combinations corresponding to the monitoring data sequence subset C0, and the correlation characterization value corresponding to each monitoring data sequence combination corresponding to the monitoring data sequence subset C0 is obtained. The non-repetition arrangement and combination is a well-known technology, so it will not be described in detail. In this embodiment, the specific process of obtaining the correlation characterization value corresponding to each monitoring data sequence combination corresponding to the monitoring data sequence subset C0 is as follows: For any monitoring data sequence combination F corresponding to the monitoring data sequence subset C0: First, the two monitoring data sequences in the monitoring data sequence combination F are respectively recorded as the first sequence and the second sequence, and it is determined whether the number of monitoring data subsequences in the monitoring data subsequence set corresponding to the first sequence is not greater than the number of monitoring data subsequences in the monitoring data subsequence set corresponding to the second sequence. If so, the first sequence is recorded as the first monitoring data sequence of the monitoring data sequence combination F, and the second sequence is recorded as the second monitoring data sequence of the monitoring data sequence combination F. Otherwise, the second sequence is recorded as the first monitoring data sequence of the monitoring data sequence combination F, and the first sequence is recorded as the second monitoring data sequence of the monitoring data sequence combination F, that is, the number of monitoring data subsequences in the monitoring data subsequence set corresponding to the first monitoring data sequence is not greater than the number of monitoring data subsequences in the monitoring data subsequence set corresponding to the second monitoring data sequence.

[0032] Then, based on the monitoring data subsequence set corresponding to the first monitoring data sequence of monitoring data sequence combination F and the monitoring data subsequence set corresponding to the second monitoring data sequence of monitoring data sequence combination F, the monitoring data subsequence pairs corresponding to each monitoring data subsequence in the monitoring data subsequence set corresponding to the first monitoring data sequence are obtained, and then the monitoring data subsequence pairs corresponding to all monitoring data subsequences in the monitoring data subsequence set corresponding to the first monitoring data sequence are recorded as the monitoring data subsequence pairs corresponding to the monitoring data sequence combination F.

[0033] Next, this embodiment will obtain monitoring data subsequence pairs corresponding to each monitoring data subsequence in the monitoring data subsequence set corresponding to the first monitoring data sequence according to the monitoring data subsequence set corresponding to the first monitoring data sequence of the monitoring data sequence combination F and the monitoring data subsequence set corresponding to the second monitoring data sequence of the monitoring data sequence combination F. For ease of understanding, this embodiment will be described below by taking the process of obtaining the monitoring data subsequence pair corresponding to the i-th monitoring data subsequence in the monitoring data subsequence set D1 corresponding to the first monitoring data sequence as an example. Therefore, the specific process of obtaining the monitoring data subsequence pair corresponding to the i-th monitoring data subsequence is as follows: First, the monitoring data subsequence set corresponding to the second monitoring data sequence of the monitoring data sequence combination F is recorded as the monitoring data subsequence set to be matched corresponding to the i-th monitoring data subsequence, and all monitoring data subsequences in the monitoring data subsequence set to be matched are recorded as monitoring data subsequences to be matched.

[0034] Then, the target difference value corresponding to each to-be-matched monitoring data subsequence in the to-be-matched monitoring data subsequence set is obtained, and the target difference value corresponding to each to-be-matched monitoring data subsequence can reflect the difference between the ith monitoring data subsequence and the corresponding to-be-matched monitoring data subsequence. Subsequently, the monitoring data subsequence pair corresponding to the ith monitoring data subsequence will be determined based on the target difference value. For ease of understanding, this embodiment will be described by taking the process of obtaining the target difference value corresponding to the jth to-be-matched monitoring data subsequence in the to-be-matched monitoring data subsequence set as an example. Therefore, the specific process of obtaining the target difference value corresponding to the jth to-be-matched monitoring data subsequence in this embodiment is as follows: If it is determined that the types of monitoring data in all monitoring data sequences in the monitoring data sequence combination F all belong to the performance data of railway facilities, the DTW distance between the ith monitoring data subsequence and the jth monitoring data subsequence to be matched is taken as the target difference value corresponding to the jth monitoring data to be matched; and the DTW distance between the ith monitoring data subsequence and the jth monitoring data subsequence to be matched can reflect the difference or similarity between the ith monitoring data subsequence and the jth monitoring data subsequence to be matched, and the larger the value of the DTW distance, the larger the difference between the ith monitoring data subsequence and the jth monitoring data subsequence to be matched, which also indicates that the target difference value corresponding to the jth monitoring data subsequence to be matched is larger, and the larger the target difference value corresponding to the jth monitoring data subsequence to be matched is, the greater the possibility that the jth monitoring data subsequence to be matched is the matching monitoring data subsequence corresponding to the ith monitoring data subsequence, and the greater the possibility that the combination of the ith monitoring data subsequence and the jth monitoring data subsequence to be matched is a monitoring data subsequence pair.

[0035] In addition, the process of using the DTW algorithm to obtain the DTW distance between the i-th monitoring data subsequence and the j-th monitoring data subsequence to be matched is a well-known technology, and thus will not be described in detail in this embodiment.

[0036] If it is determined that the types of monitoring data in all monitoring data sequences in the monitoring data sequence combination F do not belong to the performance data of railway facilities, the first eigenvalue corresponding to the i-th monitoring data subsequence and the first eigenvalue corresponding to the j-th monitoring data to be matched are obtained, and then the absolute value of the difference between the first eigenvalue corresponding to the j-th monitoring data subsequence to be matched and the first eigenvalue corresponding to the i-th monitoring data subsequence is calculated and recorded as the characteristic index value, and then the value obtained by negative correlation mapping the characteristic index value is recorded as the difference between the i-th monitoring data subsequence and the j-th monitoring data subsequence to be matched. Mapping value, then calculate the difference between the preset first constant and the mapping value, and record it as the first difference value between the i-th monitoring data subsequence and the j-th monitoring data subsequence to be matched, then calculate the absolute value of the difference between the total number of monitoring data in the j-th monitoring data subsequence to be matched and the total number of monitoring data in the i-th monitoring data subsequence, and record it as the quantity difference value between the i-th monitoring data subsequence and the j-th monitoring data subsequence to be matched; then obtain the product of the first difference value and the quantity difference value, and record it as the target difference value corresponding to the j-th monitoring data subsequence to be matched.

[0037] In this embodiment, the types of monitoring data in all monitoring data sequences in the monitoring data sequence combination F do not belong to the performance data of railway facilities, which means that the types of monitoring data in all monitoring data sequences in the monitoring data sequence combination F may all belong to fault record data or maintenance record data.

[0038] And the larger the quantity difference value and the first difference value between the i-th monitoring data subsequence and the j-th monitoring data subsequence to be matched, the larger the target difference value corresponding to the j-th monitoring data subsequence to be matched, and the larger the target difference value corresponding to the j-th monitoring data subsequence to be matched, the greater the possibility that the j-th monitoring data subsequence to be matched is the matching monitoring data subsequence corresponding to the i-th monitoring data subsequence, and also the greater the possibility that the combination of the i-th monitoring data subsequence and the j-th monitoring data subsequence to be matched is a monitoring data subsequence pair; and when the monitoring data types in all monitoring data sequences in the monitoring data sequence combination F are When the types do not belong to the performance data of railway facilities but to fault record data or maintenance record data, the reason for considering the quantity difference value and the first difference value when obtaining the target difference value corresponding to the monitoring data subsequence to be matched is that the time when two railway facilities of the same type start to be put into use or the frequency of use may be different, and then the number of monitoring data in the j-th monitoring data subsequence to be matched and the i-th monitoring data subsequence will be the same, but the location distribution of the monitoring data will be different. Therefore, in order to ensure the reliability of the target difference value corresponding to the obtained monitoring data subsequence to be matched, it is necessary to consider the quantity difference value and the first difference value.

[0039] In addition, in a specific application, the implementer may set the value of the preset first constant according to actual conditions. For example, in this embodiment, the value of the preset first constant is set to be a constant 1.

[0040] And the method for obtaining the first eigenvalue corresponding to the i-th monitoring data subsequence is: obtain the characteristic difference value sequence corresponding to the i-th monitoring data subsequence, and the h-th characteristic difference value in the characteristic difference value sequence is the time difference between the collection time corresponding to the h+1-th monitoring data in the i-th monitoring data subsequence and the collection time corresponding to the h-th monitoring data in the i-th monitoring data subsequence, and then obtain the product of the mean of the characteristic difference value sequence and the variance of the characteristic difference value sequence, and record it as the first eigenvalue corresponding to the i-th monitoring data subsequence.

[0041] In this embodiment, the method for obtaining the first characteristic value corresponding to the j-th monitoring data to be matched is the same as the method for obtaining the first characteristic value corresponding to the ith monitoring data subsequence, and therefore will not be described in detail.

[0042] In addition, in this embodiment, the specific expression for calculating the first difference value between the i-th monitoring data subsequence and the j-th monitoring data subsequence to be matched is: in, is the first difference between the i-th monitoring data subsequence and the j-th monitoring data subsequence to be matched, exp() is an exponential function with a constant e as the base, is the variance of the characteristic difference sequence corresponding to the i-th monitoring data subsequence, is the total number of characteristic difference values ​​in the characteristic difference sequence corresponding to the i-th monitoring data subsequence, is the nth characteristic difference value in the characteristic difference value sequence corresponding to the i-th monitoring data subsequence, is the variance of the feature difference sequence corresponding to the jth monitoring data subsequence to be matched, is the total number of feature difference values ​​in the feature difference value sequence corresponding to the jth monitoring data subsequence to be matched, is the mth feature difference value in the feature difference value sequence corresponding to the jth monitoring data subsequence to be matched, is the first eigenvalue corresponding to the i-th monitoring data subsequence, is the first eigenvalue corresponding to the jth monitoring data subsequence to be matched.

[0043] In the above formula, when the difference between the first eigenvalue corresponding to the i-th monitoring data subsequence and the first eigenvalue corresponding to the j-th monitoring data subsequence to be matched is larger, it indicates that The larger the value of The larger the value of , the less similar the position distribution of the monitoring data in the i-th monitoring data subsequence is to the j-th monitoring data subsequence to be matched. On the contrary, when the difference between the first eigenvalue corresponding to the i-th monitoring data subsequence and the first eigenvalue corresponding to the j-th monitoring data subsequence to be matched is, the smaller the difference is, indicating The smaller the value of The smaller the value of is, the more similar the position distribution of the monitoring data in the i-th monitoring data subsequence is to that in the j-th monitoring data subsequence to be matched.

[0044] Therefore, through the above process, the target difference values ​​corresponding to all the monitoring data subsequences to be matched in the monitoring data subsequence set to be matched can be obtained, and then the set constructed by the target difference values ​​corresponding to all the monitoring data subsequence sets to be matched in the monitoring data subsequence set to be matched is recorded as the target difference value set corresponding to the monitoring data subsequence set to be matched; and the monitoring data subsequence to be matched corresponding to the minimum target difference value is selected in the target difference value set and recorded as the matching monitoring data subsequence corresponding to the i-th monitoring data subsequence, and the combination of the i-th monitoring data subsequence and the matching monitoring data subsequence corresponding to the i-th monitoring data subsequence is recorded as the monitoring data subsequence pair corresponding to the i-th monitoring data subsequence.

[0045] Therefore, this embodiment obtains the monitoring data subsequence pair corresponding to the i-th monitoring data subsequence in the monitoring data subsequence set D1 corresponding to the first monitoring data sequence of the monitoring data sequence combination F through the above process. Therefore, according to the above method for obtaining the monitoring data subsequence pair corresponding to the i-th monitoring data subsequence, it is possible to obtain the monitoring data subsequence pairs corresponding to all monitoring data subsequences in the monitoring data subsequence set D1 corresponding to the first monitoring data sequence. In this embodiment, the monitoring data subsequence pairs corresponding to all monitoring data subsequences in the monitoring data subsequence set D1 corresponding to the first monitoring data sequence are recorded as the monitoring data subsequence pairs corresponding to the monitoring data sequence combination F.

[0046] Therefore, this embodiment obtains all monitoring data subsequence pairs corresponding to the monitoring data sequence combination F through the above process. After obtaining all monitoring data subsequence pairs corresponding to the monitoring data sequence combination F, this embodiment also needs to obtain the time difference value corresponding to each monitoring data subsequence pair, and the time difference value corresponding to the monitoring data subsequence pair is an important indicator for subsequently determining the correlation characterization value corresponding to the monitoring data sequence combination F. Therefore, the specific method for obtaining the time difference value corresponding to each monitoring data subsequence pair corresponding to the monitoring data sequence combination F in this embodiment is: For any monitoring data subsequence pair G corresponding to the monitoring data sequence combination F, the monitoring sub-time periods corresponding to the two monitoring data subsequences in the monitoring data subsequence pair G are respectively recorded as the first monitoring sub-time period and the second monitoring sub-time period, and the absolute value of the difference between the starting time in the first monitoring sub-time period and the starting time in the second monitoring sub-time period is recorded as the time difference value corresponding to the monitoring data subsequence pair G.

[0047] Therefore, through the above process, the time difference values ​​corresponding to each monitoring data subsequence pair corresponding to the monitoring data sequence combination F can be obtained, and then a set of the time difference values ​​corresponding to all monitoring data subsequence pairs corresponding to the monitoring data sequence combination F is constructed, and the constructed set is recorded as the time difference value set corresponding to the monitoring data sequence combination F, and then the normalized value of the information entropy of the time difference value set is calculated, and recorded as the correlation characterization value corresponding to the monitoring data sequence combination F; and in this embodiment, when the value of the information entropy of the time difference value set is larger, it indicates that the correlation characterization value corresponding to the monitoring data sequence combination F is larger, and the larger the correlation characterization value corresponding to the monitoring data sequence combination F is, the smaller the correlation between the two monitoring data sequences in the monitoring data sequence combination F is, and vice versa, it indicates that the correlation between the two monitoring data sequences in the monitoring data sequence combination F is larger.

[0048] Therefore, through the above process, the correlation characterization value corresponding to each monitoring data sequence combination corresponding to the monitoring data sequence subset C0 corresponding to the railway facility category B0 can be obtained, and then the mean of the correlation characterization values ​​corresponding to all monitoring data sequence combinations corresponding to the monitoring data sequence subset C0 can be obtained, and recorded as the initial importance value corresponding to the monitoring data sequence subset C0. The larger the value of the correlation characterization value corresponding to all monitoring data sequence combinations corresponding to the monitoring data sequence subset C0, the larger the initial importance value corresponding to the monitoring data sequence subset C0. The larger the initial importance value corresponding to the monitoring data sequence subset C0, the greater the weight of the monitoring data sequence in the monitoring data sequence subset C0 in the subsequent training process of the weighted linear regression prediction model.

[0049] Therefore, according to the above process, this embodiment can obtain the initial importance value corresponding to each monitoring data sequence subset corresponding to the railway facility category B0. Next, the initial importance value corresponding to each monitoring data sequence subset corresponding to the railway facility category B0 is normalized, and the normalized value is recorded as the target importance value, that is, the size of the target importance value is related to the weight of the monitoring data sequence in the monitoring data sequence subset, specifically: For the railway facility category B0, the cumulative sum of the initial importance values ​​corresponding to all monitoring data sequence subsets corresponding to the railway facility category B0 is recorded as the comprehensive characterization value corresponding to the railway facility category B0, and the ratio of the initial importance value corresponding to each monitoring data sequence subset corresponding to the railway facility category B0 to the comprehensive characterization value is recorded as the target importance value of the corresponding monitoring data sequence subset.

[0050] Based on the above process, it can be known that the size of the target importance value is related to the weight of the monitoring data sequence in the monitoring data sequence subset. Therefore, this embodiment will obtain the weight value corresponding to each monitoring data sequence in the monitoring data sequence set corresponding to each railway facility according to the target importance value of each monitoring data sequence subset corresponding to the railway facility category B0. The specific acquisition method is: For the w-th monitoring data sequence in the monitoring data sequence set corresponding to the s-th railway facility in the railway facility category B0, if the w-th monitoring data sequence is included in the r-th monitoring data sequence subset corresponding to the railway facility category B0, the target importance value of the r-th monitoring data sequence subset is used as the weight value corresponding to the w-th monitoring data sequence.

[0051] Therefore, through the above process, the weight value corresponding to each monitoring data sequence in the monitoring data sequence set corresponding to each railway facility in each railway facility category can be obtained.

[0052] Step S003, obtaining the predicted remaining life of each railway facility according to the weight value corresponding to each monitoring data sequence, and obtaining the future update frequency of each railway facility according to the predicted remaining life of each railway facility.

[0053] Next, this embodiment will obtain the predicted remaining life of each railway facility based on the weight value corresponding to each monitoring data sequence in the monitoring data sequence set corresponding to each railway facility in each railway facility category, and obtain the future update frequency corresponding to each railway facility based on the predicted remaining life of each railway facility. For ease of understanding, this embodiment will be described below by taking the acquisition process of the predicted remaining life of each railway facility in the railway facility category B0 as an example. Therefore, the specific process of obtaining the predicted remaining life of each railway facility in the railway facility category B0 is as follows: Firstly, a weighted linear regression remaining life prediction model corresponding to railway facility category B0 is constructed, and according to each monitoring data sequence in the monitoring data sequence set corresponding to each railway facility in railway facility category B0 and the weight value corresponding to each monitoring data sequence in the monitoring data sequence set corresponding to each railway facility in railway facility category B0, the weighted linear regression remaining life prediction model corresponding to railway facility category B0 is trained to obtain a trained prediction model, which is recorded as the target remaining life prediction model corresponding to railway facility category B0, and each monitoring data sequence in the monitoring data sequence set corresponding to each railway facility in railway facility category B0 is input into the target remaining life prediction model corresponding to railway facility category B0, and the predicted remaining life corresponding to each railway facility in railway facility category B0 is output.

[0054] In addition, in this embodiment, the training process of the weighted linear regression remaining life prediction model is a well-known technology, so it will not be described in detail in this embodiment.

[0055] Then, based on the predicted remaining life of each railway facility in the railway facility category B0, the future update frequency of each railway facility in the railway facility category B0 is obtained, specifically: For the s-th railway facility in the railway facility category B0: obtain the rated life corresponding to the s-th railway facility, and then obtain the ratio of the predicted remaining life corresponding to the s-th railway facility to the rated life corresponding to the s-th railway facility, and record it as the judgment index corresponding to the s-th railway facility.

[0056] In this embodiment, the rated life of the railway facilities refers to the service life of the corresponding railway facilities under normal use, and usually the rated life of the railway facilities is set by the railway facility manufacturer; in addition, this embodiment requires that the rated life be consistent with the time unit of the predicted remaining life.

[0057] Based on the process of obtaining the judgment index, it can be known that the value interval of the judgment index is [0,1], which is recorded as the judgment index value interval, and then the judgment index value interval is divided to obtain the judgment index value sub-interval corresponding to the judgment index value interval, and then the minimum value in the judgment index value sub-interval is used as the sorting value of the corresponding judgment index value sub-interval, and then all the judgment index value sub-intervals corresponding to the judgment index value interval are sorted in order from small to large according to the sorting value to obtain the judgment index value sub-interval sequence, and then the update frequency of each judgment index value sub-interval sequence in the judgment index value sub-interval sequence is obtained, and in the judgment index value sub-interval sequence, the earlier the judgment index value sub-interval is, the smaller the update frequency is.

[0058] In this embodiment, the implementer needs to set the division rules for dividing the judgment indicator value interval according to the actual situation, for example, equal division or unequal division can be performed, and the update frequency of each judgment indicator value sub-interval sequence needs to be set according to the actual situation. For example, in this embodiment, the judgment indicator value interval [0,1] can be divided into three judgment indicator value sub-intervals, namely [0,0.3), [0.3,0.7) and [0.7,1], and the update frequency of the judgment indicator value sub-interval [0,0.3) can be set to 10 days, the update frequency of the judgment indicator value sub-interval [0.3,0.7) can be set to 30 days, and the update frequency of the judgment indicator value sub-interval [0.7,1] can be set to 40 days.

[0059] Then, in the preset judgment indicator interval sequence, the update frequency of the judgment indicator value sub-interval containing the judgment indicator corresponding to the s-th railway facility is obtained, and used as the future update frequency corresponding to the s-th railway facility, and the future update frequency corresponding to the s-th railway facility refers to the next update time corresponding to the s-th railway facility; for example, if the judgment indicator corresponding to the s-th railway facility belongs to the judgment indicator value sub-interval [0.3,0.7), then the future update frequency corresponding to the s-th railway facility is 30 days, and the time interval between the most recent update time of the s-th railway facility and the next update time of the s-th railway facility is 30 days, and when updating, all monitoring data of the s-th railway facility collected in the time period between the most recent update time and the next update time of the s-th railway facility are updated to or stored in the railway knowledge graph.

[0060] Therefore, this embodiment can obtain the future update frequencies corresponding to all railway facilities in the railway facility category B0 based on the above process of obtaining the future update frequency corresponding to the s-th railway facility, and can also obtain the future update frequencies corresponding to each railway facility in other railway facility categories based on the above method of obtaining the future update frequencies corresponding to all railway facilities in the railway facility category B0.

[0061] To summarize, according to the tag value corresponding to each acquired monitoring data sequence, each monitoring data sequence subset corresponding to each railway facility category is obtained, and according to each monitoring data sequence in the acquired monitoring data sequence subset and the monitoring time period corresponding to each monitoring data sequence, a monitoring data subsequence set corresponding to each monitoring data sequence in each monitoring data sequence subset is obtained; then, according to each monitoring data subsequence in the monitoring data subsequence set, the target importance value of each monitoring data sequence subset corresponding to each railway facility category is obtained, and according to the target importance value of each monitoring data sequence subset, the weight value corresponding to each monitoring data sequence in the monitoring data sequence set corresponding to each railway facility is obtained; finally, according to the weight value corresponding to each monitoring data sequence, the predicted remaining life corresponding to each railway facility is obtained, and according to the predicted remaining life corresponding to each railway facility, the future update frequency corresponding to each railway facility is obtained. This embodiment can more reliably predict the remaining life of each railway facility by obtaining the weight value corresponding to each monitoring data sequence in the monitoring data sequence set corresponding to each railway facility through the target importance value of each monitoring data sequence subset, that is, can more reliably obtain the predicted remaining life of each railway facility, and the future update frequency of each railway facility obtained based on the predicted remaining life of each railway facility can not only reduce the resource consumption when updating the railway knowledge graph, but also ensure the efficiency and accuracy of railway management.

[0062] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A railway knowledge graph data update frequency evaluation method based on life cycle prediction, characterized in that: The method comprises the following steps: Obtain a monitoring data sequence set corresponding to each railway facility in each railway facility category in the railway facility category set corresponding to the railway knowledge graph, a monitoring time period corresponding to each monitoring data sequence in the monitoring data sequence set, and a tag value corresponding to each monitoring data sequence; Obtaining subsets of monitoring data sequences corresponding to each railway facility category according to the tag values ​​corresponding to each monitoring data sequence; According to each monitoring data sequence in the monitoring data sequence subset and the monitoring time period corresponding to each monitoring data sequence, obtaining a monitoring data subsequence set corresponding to each monitoring data sequence in each monitoring data sequence subset; According to each monitoring data subsequence in the monitoring data subsequence set, a target importance value of each monitoring data sequence subset corresponding to each railway facility category is obtained; Obtaining, according to the target importance values ​​of each monitoring data sequence subset, a weight value corresponding to each monitoring data sequence in the monitoring data sequence set corresponding to each railway facility; According to the weight values ​​corresponding to the monitoring data sequences, the predicted remaining life of each railway facility is obtained, and according to the predicted remaining life of each railway facility, the future update frequency of each railway facility is obtained.

2. The railway knowledge graph data update frequency evaluation method based on life cycle prediction according to claim 1 is characterized in that: A method for obtaining a monitoring data sequence set corresponding to each railway facility in each railway facility category in a railway facility category set, a monitoring time period corresponding to each monitoring data sequence in the monitoring data sequence set, and a tag value corresponding to each monitoring data sequence, comprising: Record the set constructed by all railway facilities in the railway knowledge graph as the railway facility set, and obtain all railway facility categories corresponding to the railway facility set by statistics, and record the set constructed by all railway facility categories corresponding to the railway facility set as the railway facility category set corresponding to the railway knowledge graph; For any railway facility a0 in any railway facility category B0 in the railway facility category set: in the current monitoring time period corresponding to the railway facility a0, all monitoring data corresponding to the railway facility a0 are obtained, and the set constructed by all monitoring data corresponding to the railway facility a0 is recorded as the monitoring data set corresponding to the railway facility a0, and the types of all monitoring data in the monitoring data set corresponding to the railway facility a0 are statistically obtained, and all are recorded as the monitoring data type corresponding to the railway facility a0; in the monitoring data set corresponding to the railway facility a0, a sequence constructed by monitoring data of the same monitoring data type is recorded as the monitoring data sequence corresponding to the railway facility a0, and the monitoring data in the monitoring data sequence are arranged in the order of collection time; the set constructed by all monitoring data sequences corresponding to the railway facility a0 is recorded as the monitoring data sequence set corresponding to the railway facility a0; the current monitoring time period corresponding to the railway facility a0 is taken as the monitoring time period corresponding to each monitoring data sequence in the monitoring data sequence set corresponding to the railway facility a0, and the time period formed from the start of commissioning of the railway facility a0 to the current moment is the current monitoring time period corresponding to the railway facility a0; For the railway facility category B0: the monitoring data sequence sets corresponding to each railway facility in the railway facility category B0 are all recorded as the feature sequence sets corresponding to the railway facility category B0, the mark values ​​corresponding to the c-th monitoring data sequence in each feature sequence set corresponding to the railway facility category B0 are all recorded as c, and the monitoring data types in the c-th monitoring data sequence in each feature sequence set are the same.

3. The railway knowledge graph data update frequency evaluation method based on life cycle prediction according to claim 2 is characterized in that: The method of obtaining the subsets of monitoring data sequences corresponding to the railway facility categories according to the tag values ​​corresponding to the monitoring data sequences comprises: For the railway facility category B0: a new set constructed by the set of monitoring data sequences corresponding to all railway facilities in the railway facility category B0 is recorded as the comprehensive monitoring data sequence set corresponding to the railway facility category B0; a set constructed by all monitoring data sequences with the same mark value in the comprehensive monitoring data sequence set is recorded as the monitoring data sequence subset corresponding to the railway facility category B0.

4. The railway knowledge graph data update frequency evaluation method based on life cycle prediction according to claim 2 is characterized in that: The method for obtaining a monitoring data subsequence set corresponding to each monitoring data sequence in each monitoring data sequence subset comprises: For the monitoring time period T0 corresponding to any monitoring data sequence A0 in any monitoring data sequence subset C0 corresponding to the railway facility category B0: The monitoring time period T0 is evenly divided to obtain all monitoring sub-time periods corresponding to the monitoring time period T0; for any monitoring sub-time period t0 corresponding to the monitoring time period T0, in the monitoring data sequence A0, a sequence constructed by all monitoring data whose collection time is in the monitoring sub-time period t0 is recorded as the monitoring data sub-sequence corresponding to the monitoring sub-time period t0; For the monitoring data sequence A0, a subsequence set constructed by monitoring data subsequences corresponding to all monitoring sub-time periods corresponding to the monitoring time period T0 is recorded as the monitoring data subsequence set corresponding to the monitoring data sequence A0.

5. The railway knowledge graph data update frequency evaluation method based on life cycle prediction according to claim 4 is characterized in that: The method for obtaining the target importance value of each monitoring data sequence subset corresponding to each railway facility category includes: For the monitoring data sequence subset C0 corresponding to the railway facility category B0: Perform non-repeating permutations and combinations on any two monitoring data sequences in the monitoring data sequence subset C0 to obtain all monitoring data sequence combinations corresponding to the monitoring data sequence subset C0, and obtain correlation characterization values ​​corresponding to each monitoring data sequence combination corresponding to the monitoring data sequence subset C0; record the average of the correlation characterization values ​​corresponding to all monitoring data sequence combinations corresponding to the monitoring data sequence subset C0 as the initial importance value corresponding to the monitoring data sequence subset C0; For the railway facility category B0, the cumulative sum of the initial importance values ​​corresponding to all monitoring data sequence subsets corresponding to the railway facility category B0 is recorded as the comprehensive characterization value corresponding to the railway facility category B0, and the ratio of the initial importance value corresponding to each monitoring data sequence subset corresponding to the railway facility category B0 to the comprehensive characterization value is recorded as the target importance value of the corresponding monitoring data sequence subset.

6. The railway knowledge graph data update frequency evaluation method based on life cycle prediction according to claim 5 is characterized in that: The method for obtaining the correlation characterization value corresponding to each monitoring data sequence combination includes: For any monitoring data sequence combination F corresponding to the monitoring data sequence subset C0: The two monitoring data sequences in the monitoring data sequence combination F are respectively recorded as the first monitoring data sequence and the second monitoring data sequence of the monitoring data sequence combination F, and the number of monitoring data subsequences in the monitoring data subsequence set corresponding to the first monitoring data sequence is not greater than the number of monitoring data subsequences in the monitoring data subsequence set corresponding to the second monitoring data sequence; According to the monitoring data subsequence set corresponding to the first monitoring data sequence and the monitoring data subsequence set corresponding to the second monitoring data sequence, obtaining monitoring data subsequence pairs corresponding to each monitoring data subsequence in the monitoring data subsequence set corresponding to the first monitoring data sequence, and recording the monitoring data subsequence pairs corresponding to all monitoring data subsequences in the monitoring data subsequence set corresponding to the first monitoring data sequence as monitoring data subsequence pairs corresponding to the monitoring data sequence combination F; For any monitoring data subsequence pair G corresponding to the monitoring data sequence combination F, the monitoring sub-time periods corresponding to the two monitoring data subsequences in the monitoring data subsequence pair G are respectively recorded as the first monitoring sub-time period and the second monitoring sub-time period, and the absolute value of the difference between the starting time in the first monitoring sub-time period and the starting time in the second monitoring sub-time period is recorded as the time difference value corresponding to the monitoring data subsequence pair G; the set constructed by the time difference values ​​corresponding to all monitoring data subsequence pairs corresponding to the monitoring data sequence combination F is recorded as the time difference value set corresponding to the monitoring data sequence combination F, and the normalized value of the information entropy of the time difference value set is recorded as the correlation characterization value corresponding to the monitoring data sequence combination F.

7. The railway knowledge graph data update frequency evaluation method based on life cycle prediction according to claim 6 is characterized in that: The method for obtaining a monitoring data subsequence pair corresponding to each monitoring data subsequence in a monitoring data subsequence set corresponding to the first monitoring data sequence includes: For the i-th monitoring data subsequence in the monitoring data subsequence set D1 corresponding to the first monitoring data sequence: Recording the set of monitoring data subsequences corresponding to the second monitoring data sequence as the set of monitoring data subsequences to be matched corresponding to the i-th monitoring data subsequence; Obtain target difference values ​​corresponding to each to-be-matched monitoring data subsequence in the to-be-matched monitoring data subsequence set, and record a set constructed by target difference values ​​corresponding to all to-be-matched monitoring data subsequences in the to-be-matched monitoring data subsequence set as a target difference value set; select a to-be-matched monitoring data subsequence corresponding to a minimum target difference value in the target difference value set as a matching monitoring data subsequence corresponding to the ith monitoring data subsequence; and record a combination of the ith monitoring data subsequence and the matching monitoring data subsequence corresponding to the ith monitoring data subsequence as a monitoring data subsequence pair corresponding to the ith monitoring data subsequence.

8. The railway knowledge graph data update frequency evaluation method based on life cycle prediction according to claim 7 is characterized in that: The method for obtaining the target difference value corresponding to each to-be-matched monitoring data subsequence in the to-be-matched monitoring data subsequence set includes: For the jth monitoring data subsequence to be matched in the set of monitoring data subsequences to be matched: If it is determined that the types of monitoring data in all monitoring data sequences in the monitoring data sequence combination F belong to the performance data of railway facilities, the DTW distance between the i-th monitoring data subsequence and the j-th monitoring data subsequence to be matched is used as the target difference value corresponding to the j-th monitoring data to be matched; If it is determined that the types of monitoring data in all monitoring data sequences in the monitoring data sequence combination F do not belong to the performance data of railway facilities, then the first eigenvalue corresponding to the i-th monitoring data subsequence and the first eigenvalue corresponding to the j-th monitoring data to be matched are obtained, and the method for obtaining the first eigenvalue corresponding to the j-th monitoring data to be matched is the same as the method for obtaining the first eigenvalue corresponding to the i-th monitoring data subsequence, and the absolute value of the difference between the first eigenvalue corresponding to the j-th monitoring data subsequence to be matched and the first eigenvalue corresponding to the i-th monitoring data subsequence is recorded as the characteristic index value, and the value obtained by negatively correlating the characteristic index value is recorded as the mapping value between the i-th monitoring data subsequence and the j-th monitoring data subsequence to be matched, and the difference between the preset first constant and the mapping value is recorded as the first difference value, and the absolute value of the difference between the total number of monitoring data in the j-th monitoring data subsequence to be matched and the total number of monitoring data in the i-th monitoring data subsequence is recorded as the quantity difference value, and the product of the first difference value and the quantity difference value is recorded as the target difference value corresponding to the j-th monitoring data subsequence to be matched; The method for obtaining the first eigenvalue corresponding to the i-th monitoring data subsequence includes: Obtain a feature difference value sequence corresponding to the ith monitoring data subsequence, where the hth feature difference value in the feature difference value sequence is the time difference between the collection time corresponding to the h+1th monitoring data in the ith monitoring data subsequence and the collection time corresponding to the hth monitoring data in the ith monitoring data subsequence; and record the product of the mean of the feature difference value sequence and the variance of the feature difference value sequence as the first eigenvalue corresponding to the ith monitoring data subsequence.

9. The railway knowledge graph data update frequency evaluation method based on life cycle prediction according to claim 4 is characterized in that: The method for obtaining the weight value corresponding to each monitoring data sequence in the monitoring data sequence set corresponding to each railway facility comprises: For the w-th monitoring data sequence in the monitoring data sequence set corresponding to the s-th railway facility in the railway facility category B0, if the w-th monitoring data sequence is included in the r-th monitoring data sequence subset corresponding to the railway facility category B0, the target importance value of the r-th monitoring data sequence subset is used as the weight value corresponding to the w-th monitoring data sequence.

10. The railway knowledge graph data update frequency evaluation method based on life cycle prediction according to claim 9, characterized in that: The method of obtaining the predicted remaining life of each railway facility according to the weight value corresponding to each monitoring data sequence, and obtaining the future update frequency of each railway facility according to the predicted remaining life of each railway facility, comprises: For the railway facility category B0: according to each monitoring data sequence in the monitoring data sequence set corresponding to each railway facility in the railway facility category B0 and the weight value corresponding to each monitoring data sequence in the monitoring data sequence set corresponding to each railway facility in the railway facility category B0, the weighted linear regression remaining life prediction model corresponding to the railway facility category B0 is trained to obtain the trained weighted linear regression remaining life prediction model corresponding to the railway facility category B0, and recorded as the target remaining life prediction model corresponding to the railway facility category B0, each monitoring data sequence in the monitoring data sequence set corresponding to each railway facility in the railway facility category B0 is input into the target remaining life prediction model corresponding to the railway facility category B0, and the predicted remaining life corresponding to each railway facility in the railway facility category B0 is output; For the s-th railway facility in the railway facility category B0: obtaining the rated life corresponding to the s-th railway facility, and recording the ratio of the predicted remaining life corresponding to the s-th railway facility to the rated life corresponding to the s-th railway facility as the determination index corresponding to the s-th railway facility; Obtaining a determination indicator value interval, and dividing the determination indicator value interval to obtain all determination indicator value sub-intervals corresponding to the determination indicator value interval, taking the minimum value in the determination indicator value sub-interval as the ranking value of the corresponding determination indicator value sub-interval, and sorting all determination indicator value sub-intervals corresponding to the determination indicator value interval in ascending order of the ranking value, to obtain a determination indicator value sub-interval sequence and an update frequency of each determination indicator value sub-interval sequence in the determination indicator value sub-interval sequence, wherein the earlier the determination indicator value sub-interval in the determination indicator value sub-interval sequence is, the smaller the update frequency is; In the preset determination index interval sequence, the update frequency of the determination index value sub-interval including the determination index corresponding to the s-th railway facility is obtained and used as the future update frequency corresponding to the s-th railway facility.

Images