Rail train fusion host-oriented data integration method

Abstract

The invention relates to the technical field of data processing, in particular to a rail train fusion host-oriented data integration method, which comprises the following steps of: acquiring and preprocessing multi-dimensional time sequence data when a rail train runs; determining a target dimension between a target shift and a non-target shift based on the preprocessed multi-dimensional time series data, obtaining corresponding specificity through the target dimension, constructing a target dimension set, obtaining a weight of each dimension of the target shift, and determining a non-standard factor of the target shift; determining a first attention degree and a second attention degree of each initial time period based on the non-standard factors and the multi-dimensional time sequence data, and constructing an attention degree sequence of the target shift from the starting point to the end point by combining the first attention degrees and the second attention degrees to obtain a plurality of optimal time periods and corresponding attention degrees; and carrying out parallel coding on all dimension time sequence data corresponding to the optimal time period, and taking the attention corresponding to the optimal time period as a label to complete data integration.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a data integration method for a rail train fusion host. Background Art

[0002] The data of rail trains is diverse, including detailed information on train operation status, passenger information, environmental monitoring, etc. The amount of data is very large, which brings huge challenges to data processing and storage. In the data integration process for rail train fusion hosts, data compression and storage are essential steps. If all the data of each train operation is directly compressed and stored uniformly, the compression efficiency is low. In addition, since the data is uniformly compressed, all data needs to be uniformly decompressed during subsequent data analysis, which consumes a lot of computer resources and is time-consuming, affecting the overall data processing efficiency. Summary of the invention

[0003] In order to solve the technical problem that the existing unified compression of rail train processing data is inefficient and unfavorable for subsequent decoding and analysis, the purpose of the present invention is to provide a data integration method for rail train fusion host, and the technical solution adopted is as follows: Collect multi-dimensional time series data of rail train operation and perform preprocessing; Define any train in the rail train as the target train, determine the target dimension between the target train and the non-target train based on the preprocessed multi-dimensional time series data, obtain the corresponding specificity through the target dimension, and construct the target dimension set to obtain the weight of each dimension of the target train; analyze the dissimilarity between the target train and the non-target train, and determine the non-standard factor of the target train; The operation process of a rail train includes multiple initial time periods. The first attention level and the second attention level of each initial time period are determined based on non-standard factors and multi-dimensional time series data. The attention level sequence of the target train from the starting point to the end point is constructed by combining the first attention level and the second attention level, and multiple optimal time periods and corresponding attention levels are obtained. All dimensional time series data corresponding to the optimal period are encoded in parallel, and the attention corresponding to the optimal period is used as a label to complete data integration.

[0004] Preferably, multi-dimensional time series data of the rail train during operation is collected and preprocessed, wherein the multi-dimensional time series data includes any data of speed, acceleration, train motor temperature, brake cylinder pressure, vibration data time series sequence and real-time position coordinates from the starting point to the end point, and the preprocessing method is standardized processing.

[0005] Preferably, any train in the rail train is defined as a target train, and the target dimension between the target train and the non-target train is determined based on the preprocessed multi-dimensional time series data. The corresponding specificity is obtained through the target dimension, and a target dimension set is constructed to obtain the weight of each dimension of the target train, including: Define two adjacent stations as Station 1 and Station 2, obtain the time series of each dimension from Station 1 to Station 2 based on the target shift and any non-target shift, calculate the dynamic time warping distance of the time series of the same dimension, and sort them from large to small. The dimension corresponding to the largest dynamic time warping distance is the target dimension, and the target dimension corresponding to the target shift and each non-target shift is obtained; Calculate the specificity corresponding to each target dimension, build a target dimension set, count the number of times each identical target dimension appears, calculate the weight corresponding to each identical target dimension, assign the weight of the dimension in the non-target dimension set, and obtain the weight of each dimension of the target shift.

[0006] Preferably, the calculation formula for the specificity corresponding to each target dimension is: in, Indicates the specificity of the target dimension; Indicates the dynamic time warping distance corresponding to the target dimension; Represents the mean of the dynamic time warping distances of all non-target dimensions.

[0007] Preferably, the calculation formula for the weight corresponding to each same target dimension is: in, Indicates the target dimension set The weights of the same target dimension; Indicates the total number of target dimensions in the target dimension set; Indicates the first The total number of the same target dimensions; Indicates the first The maximum value of the specificity of all target dimensions in the same target dimension; represents the linear normalization function.

[0008] Preferably, analyzing the dissimilarity between the target shift and the non-target shift to determine the non-standard factor of the target shift includes: Calculate the dissimilarity between the target shift and each non-target shift from station one to station two to determine the cluster distance; All train schedules are divided into multiple clusters based on clustering distance, and the cluster with the largest number of schedules is defined as the standard cluster. The clustering distance between the target schedule and the cluster center of the standard cluster is obtained to obtain the non-standard factor of the target schedule.

[0009] Preferably, the calculation formula of dissimilarity is: in, Indicates dissimilarity; Represents the total number of different dimensions in all dimensions; Indicates the target shift from station 1 to station 2. The weight of each dimension; Indicates the non-target shift from station 1 to station 2. The weight of each dimension; Indicates the target shift and non-target shift from station one to station two. Dynamic time warping distance of time series in one dimension.

[0010] Preferably, the rail train operation process includes a plurality of initial time periods, and the first attention level and the second attention level of each initial time period are respectively determined based on the non-standard factors and the multi-dimensional time series data, including: The initial period includes the start-up acceleration stage, the uniform speed running stage and the deceleration braking stage during the operation of the rail train. The first attention level of the target train in the start-up acceleration stage and the deceleration braking stage is determined by the non-standard factor. Nonstandard factors; The uniform speed running stage is divided into several small sequence segments according to the duration, and the peaks and troughs of each small sequence segment in the uniform speed running stage are obtained to calculate the speed instability corresponding to the uniform speed running stage. The corresponding calculation formula is: in, Indicates speed instability; represents the variance of all speeds during the uniform speed running stage; It indicates the maximum value of the sudden change of speed of all wave crests in the uniform speed running stage; Indicates the duration of the uniform speed running phase; Indicates the total number of wave peaks in the uniform speed running stage; Calculate the initial adjustment factor of attention in the uniform speed running stage. The corresponding calculation formula is: in, represents the initial adjustment factor of attention; Indicates speed instability; It represents the mean value of all cruising speeds during the uniform speed operation stage; Indicates the cruise speed scheduled during the uniform speed operation phase; Determine the rail train position coordinates at the start and end times of the uniform speed operation phase, construct a speed standard sequence, and determine the second level of attention in the uniform speed operation phase.

[0011] Preferably, the calculation formula of the second attention degree is: in, Indicates the second level of attention; represents a nonstandard factor; Indicates the sequence composed of all sequence sub-segments in the uniform speed running stage; Indicates the speed standard sequence; express and Dynamic time warping distance of represents the linear normalization function.

[0012] Preferably, the first attention level and the second attention level are combined to construct an attention level sequence from the starting point to the end point of the target shift, and multiple optimal time periods and corresponding attention levels are obtained, including: Determine the attention level of each initial period based on the first attention level and the second attention level, and construct the attention levels of all initial periods into an attention level sequence from the starting point to the end point of the target shift; The attention sequence is divided into multiple sequence segments by clustering, all initial time periods corresponding to each sequence segment are merged to form each optimal time period, the mean of the attention of all initial time periods in the corresponding sequence segment is calculated, and the attention of each optimal time period is determined.

[0013] The present invention has the following beneficial effects: The data integration method for rail train fusion host refers to a method of integrating and uniformly processing data from different systems and equipment on rail trains. Through data integration, data sharing, analysis and application can be realized to improve the safety, efficiency and service quality of train operation. It determines the weights between different train shifts by collecting data, analyzes the data to determine the optimal time period of the shift, and uses block Huffman coding based on the optimal time period to encode the data of each time period in parallel, thereby improving the coding efficiency, realizing segmented compression, and further improving the compression efficiency. This allows subsequent rail train operation problem analysis to only require decoding of the data of the optimal time period of the problem, thereby improving the processing efficiency of the integrated data. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] 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.

[0015] Figure 1 A schematic diagram of the steps of a data integration method for a rail train fusion host provided by an embodiment of the present invention; Figure 2 A schematic diagram of collecting multi-dimensional time series data of a data integration method for a rail train fusion host provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0016] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following is a detailed description of the data integration method for a rail train fusion host proposed by the present invention, its specific implementation method, structure, features and effects, in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0017] 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 belongs.

[0018] The specific scheme of the data integration method for a rail train fusion host provided by the present invention is described in detail below with reference to the accompanying drawings.

[0019] See also Figure 1 , which shows a schematic diagram of the steps of a data integration method for a rail train fusion host provided by an embodiment of the present invention, the method comprising: Step S1: Collect multi-dimensional time series data of rail train operation and perform preprocessing; Step S2: define any train in the rail train as a target train, determine the target dimension between the target train and the non-target train based on the preprocessed multi-dimensional time series data, obtain the corresponding specificity through the target dimension, and construct a target dimension set to obtain the weight of each dimension of the target train; analyze the dissimilarity between the target train and the non-target train, and determine the non-standard factor of the target train; Step S3: The rail train operation process includes multiple initial time periods. The first attention level and the second attention level of each initial time period are determined based on the non-standard factors and the multi-dimensional time series data. The attention level sequence of the target train from the starting point to the end point is constructed by combining the first attention level and the second attention level, and multiple optimal time periods and corresponding attention levels are obtained. Step S4: Encode all dimensional time series data corresponding to the optimal time period in parallel, and use the attention level corresponding to the optimal time period as a label to complete data integration.

[0020] It is explained that the data integration method for rail train fusion host refers to the integration of data from different sources to improve the safety, efficiency and maintainability of the rail transit system. For this purpose, fault detection and predictive maintenance can be carried out to reduce downtime; passenger flow data and real-time operation data can be integrated to optimize train scheduling and improve transportation efficiency. Integrating this method with the host to realize intelligent and automated analysis will help achieve more efficient rail transit system management.

[0021] See also Figure 2 , which shows a schematic diagram of the collection of multi-dimensional time series data for a data integration method for a rail train fusion host.

[0022] Furthermore, step S1 is that the multi-dimensional time series data includes any data of speed, acceleration, train motor temperature, brake cylinder pressure, vibration data time series sequence and real-time position coordinates from the starting point to the end point, and the preprocessing method is standardized processing.

[0023] As an optional implementation, the most common operation mode in railway track transportation is "fixed line operation" or "circular operation", that is, fixed starting and end points, periodic schedules, repeated routes and standardized operations. Since trains on the same line need to adapt to the same track conditions, signal systems and operating requirements, the train models on the same line are usually basically similar. Therefore, in this embodiment, multi-dimensional time series data of rail trains from the starting point to the end point is collected based on any fixed track line.

[0024] Specifically, speed sensors, acceleration sensors, temperature sensors, pressure sensors, vibration sensors, etc. are installed at corresponding positions on the rail train to monitor the running status of the rail train in real time, and determine the speed, acceleration, train motor temperature, brake cylinder pressure and vibration data time series sequence of each rail train from the starting point to the end point; then the real-time position coordinates of the rail train are tracked through GPS (Global Positioning System); then the multi-dimensional time series data collected above are standardized, such as minimum-maximum standardization, z-score (Standard Score) standardization, etc., to unify the dimensions to eliminate the impact of different dimensions, ensure that the data is compared and analyzed at the same level, and improve data processing efficiency.

[0025] Furthermore, in step S2, any train in the rail train is defined as a target train, and the target dimension between the target train and the non-target train is determined based on the preprocessed multi-dimensional time series data. The corresponding specificity is obtained through the target dimension, and a target dimension set is constructed to obtain the weight of each dimension of the target train, including: Step S211: define two adjacent sites as site one and site two, obtain the time series of each dimension from site one to site two based on the target shift and any non-target shift, calculate the dynamic time warping distance of the time series of the same dimension, and sort them from large to small. The dimension corresponding to the largest dynamic time warping distance is the target dimension, and the target dimension corresponding to the target shift and each non-target shift is obtained.

[0026] To explain, Dynamic Time Warping, or DTW, is a technique used to measure and compare the similarity between two time series. It adjusts the time points in the time series and elastically stretches or compresses the time series to find the best match, so that the two sequences are aligned in time to more accurately compare their shapes and patterns.

[0027] Specifically, the dynamic time warping distance of the time series of the same dimension is calculated. At this time, the smaller the DTW distance between the target shift and any non-target shift corresponding to the time series of the same dimension, the more similar the time series between the two shifts are; all the calculated dynamic time warping distances are sorted from large to small, among which the dimension corresponding to the largest dynamic time warping distance is recorded as the target dimension; similarly, the target dimension corresponding to the target shift and each non-target shift is calculated.

[0028] It is understandable that when the environment of a rail train changes, the impact on its operating data is comprehensive; due to environmental changes, the changes in the operating data of different shifts in various dimensions are usually relatively small, which is a change within the normal range; and the impact caused by the fault is more concentrated and specific, and the data changes in specific dimensions of different shifts are relatively significant, which is an important change. Therefore, different weights should be given to the similarities of data in different dimensions to enhance the reliability of data analysis.

[0029] Step S212: Calculate the specificity corresponding to each target dimension, construct a target dimension set, count the number of occurrences of each identical target dimension, calculate the weight corresponding to each identical target dimension, assign the weight of the dimension in the non-target dimension set, and obtain the weight of each dimension of the target shift.

[0030] It is explained that specificity refers to specific changes in rail trains that may cause failures, which means that the dimension of the current analysis belongs to the category of important changes, indicating that certain changes in rail trains may have a significant impact on the overall function or stability.

[0031] Furthermore, in step S212, the calculation formula for the specificity corresponding to each target dimension is: in, Indicates the specificity of the target dimension; Indicates the dynamic time warping distance corresponding to the target dimension; Represents the mean of the dynamic time warping distances of all non-target dimensions.

[0032] It can be explained that the greater the specificity of the target dimension, that is, the greater the difference between the dynamic time warping distance corresponding to the target dimension and the mean of the dynamic time warping distances of all non-target dimensions, it means that the performance of the target dimension in the time series is significantly different from that of other dimensions, which means that the target dimension is more likely to be a specific dimension of the fault. At this time, a larger weight is given to this dimension to improve the accuracy of fault detection for subsequent detection.

[0033] For better explanation, in this embodiment, a target dimension set is constructed, that is, a series of key measurement standards and indicators are clarified and set for multiple dimensions in the target dimension, so as to facilitate effective evaluation and monitoring in projects or tasks.

[0034] Specifically, as can be seen above, the target dimension set includes the target shift The target dimension of all other non-target shifts can be any multi-dimensional time series data such as speed, acceleration, temperature, etc. Now we will briefly explain based on speed and acceleration, and the target dimension data set constructed is ,in, represents the velocity dimension, represents the acceleration dimension, Indicates the target shift The target dimension of other non-target shifts 1 is the speed dimension ; Indicates the target shift The target dimension of the other non-target shift 4 is the acceleration dimension , , , Similarly; that is, the target dimension set is {target shift Target dimension with other non-target shift 1, target shift Target dimensions with other non-target shifts 2, …, target shifts Other non-target flights 's target dimension}.

[0035] Furthermore, in step S212, the calculation formula for the weight corresponding to each same target dimension is: in, Indicates the target dimension set The weights of the same target dimension; Indicates the total number of target dimensions in the target dimension set; Indicates the first The total number of the same target dimensions; Indicates the first The maximum value of the specificity of all target dimensions in the same target dimension; represents the linear normalization function.

[0036] It can be understood that in the target dimension set, the number of occurrences of each identical target dimension is counted. The more times it appears, the more it indicates that when the target shift is analyzed for similarity with other non-target shifts, the target dimension is basically a fault dimension, so it should be given a larger weight.

[0037] As an optional implementation, weights are assigned to dimensions in the non-target dimension set, that is, for dimensions that are not in the target dimension set, the weight of the dimension is assigned to be 1, and the weight of each dimension of the target shift is obtained by combining the calculation of the above weights.

[0038] Furthermore, in step S2, the dissimilarity between the target shift and the non-target shift is analyzed to determine the non-standard factors of the target shift, including: Step S221: Calculate the dissimilarity between the target shift and each non-target shift from station one to station two, and determine the clustering distance.

[0039] Furthermore, in step S221, the calculation formula of dissimilarity is: in, Indicates dissimilarity; Represents the total number of different dimensions in all dimensions; Indicates the target shift from station 1 to station 2. The weight of each dimension; Indicates the non-target shift from station 1 to station 2. The weight of each dimension; Indicates the target shift and non-target shift from station one to station two. Dynamic time warping distance of time series in one dimension.

[0040] For better explanation, dissimilarity refers to the dissimilarity between the operating data of the target shift and any non-target shift from station one to station two; based on this, the non-standard factors are analyzed, which represent the significantly different characteristics or factors between the target shift and any non-target shift from station one to station two, which may include but are not limited to differences in speed, differences in shift time, inconsistency in employee allocation, particularity in task allocation, and variations in work processes.

[0041] To illustrate, the dissimilarity between the target shift and any non-target shift is determined as the cluster distance.

[0042] Step S222: Divide all train trips into multiple clusters based on cluster distance, define the cluster with the largest number of trips as the standard cluster, obtain the cluster distance between the target train and the cluster center of the standard cluster, and obtain the non-standard factor of the target train.

[0043] Specifically, a K-means clustering algorithm is used to divide all shifts into multiple clusters, that is, the operating data of all shifts in each cluster from site one to site two are similar; wherein, the K-means clustering algorithm is an unsupervised learning algorithm widely used in the field of data mining and machine learning, and its main purpose is to divide the sample points in the data set into K clusters, so that the sample points in each cluster are as similar as possible, while the sample points between different clusters are as different as possible, that is, this goal is achieved by iteratively optimizing the sum of squared errors within the cluster; in this embodiment, the K value is obtained according to the elbow method, that is, by calculating the sum of squared errors under different numbers of clusters, and then drawing a chart, the horizontal axis represents the number of clusters, and the vertical axis represents the sum of squared errors, and by observing the curve in the chart, a point in the shape of an "elbow" is found, that is, the point where the sum of squared errors begins to significantly slow down, and the corresponding number of clusters is confirmed as the optimal K value.

[0044] It can be explained that the number of problems that occur in rail train operation is smaller than the normal number, and the probability of the same operation problem occurring multiple times is even lower. Therefore, the cluster with the largest number of trains is used as the standard cluster, that is, the time series of each dimension of the rail train from station one to station two corresponding to the cluster center of the standard cluster is used as the time series of each dimension of the rail train from station one to station two. It covers the entire process from departure from station one to arrival at station two, the performance of the rail train in various dimensions, including but not limited to speed, acceleration, energy consumption, passenger flow and other aspects. By analyzing the time series of these standard clusters, we can better understand and evaluate the performance of rail trains under normal operating conditions, thereby providing strong data support for subsequent maintenance, optimization and decision-making.

[0045] Specifically, the clustering distance between the target shift and the cluster center of the standard cluster cluster is used as the non-standard factor of the target shift's operating data from station one to station two. The larger the clustering distance, the greater the difference between the target shift's time series of each dimension from station one to station two and the standard time series of each dimension, and the greater the possibility that there is an operating problem between the target shift from station one to station two.

[0046] Further, in step S3, the rail train operation process includes a plurality of initial time periods, and the first attention level and the second attention level of each initial time period are respectively determined based on the non-standard factors and the multi-dimensional time series data, including: Step S311: The initial period includes the start-up acceleration phase, the uniform speed operation phase and the deceleration braking phase during the operation of the rail train. The first attention level of the target train in the start-up acceleration phase and the deceleration braking phase is determined by the non-standard factor. Nonstandard factors.

[0047] Preferably, it is known that the speed of the rail train can directly reflect the operating efficiency of the rail train. Therefore, in the embodiment, the analysis is mainly based on the speed dimension; among them, the start time and end time of each stage of the starting acceleration stage, the uniform speed operation stage and the deceleration braking stage are known, and this information can be obtained through the train control system, the data recorded by the on-board equipment, and the monitoring system such as the ground monitoring system.

[0048] It is explained that since the start-stop phase of a rail train is one of the phases in which accidents are most likely to occur during train operation, both the start-up acceleration phase and the deceleration and braking phase require precise control to avoid collision, derailment or other safety accidents; therefore, the first attention level of the target shift in the start-up acceleration phase and the deceleration and braking phase is Non-standard factors, i.e. .

[0049] Step S312: Divide the uniform speed running stage into a number of sequence segments according to the duration, obtain the peaks and troughs of each sequence segment in the uniform speed running stage, and calculate the speed instability corresponding to the uniform speed running stage. The corresponding calculation formula is: in, Indicates speed instability; represents the variance of all speeds during the uniform speed running stage; It indicates the maximum value of the sudden change of speed of all wave crests in the uniform speed running stage; Indicates the duration of the uniform speed running phase; Indicates the total number of peaks during the uniform speed running stage.

[0050] To better illustrate, the speed of a rail train usually remains relatively stable during the uniform speed operation phase. When the speed fluctuates greatly, it indicates that there is a problem with the operation of the rail train.

[0051] Specifically, the target shift is used for explanation, that is, the target shift is divided into multiple initial time periods from station one to station two, and the initial time period includes the starting acceleration stage, the uniform speed running stage and the deceleration and braking stage during the operation of the rail train. Then the uniform speed running stage is divided into several sequence small segments according to the duration. Preferably, in this embodiment, the uniform speed running stage is divided into 10 sequence small segments according to the duration. At this time, the initial time period is {starting acceleration stage, uniform speed running small segment 1, uniform speed running small segment 2, ..., uniform speed running small segment 10, deceleration and braking stage}.

[0052] Through the peak and trough detection algorithm, the peaks and troughs of each small segment of the sequence in the uniform speed running stage are obtained; and the speed difference and time interval between each peak and the adjacent trough are counted, and the ratio of the speed difference to the time interval is used as the speed sudden change of each peak; among them, the peak and trough detection algorithm is a technology used to identify and analyze local maximum and minimum values ​​in data sequences. A peak refers to a local maximum point in a data sequence, and the values ​​on both sides are smaller than it; and a trough refers to a local minimum point, and the values ​​on both sides are larger than it.

[0053] To explain, It represents the variance of all speeds during the uniform speed operation stage. The larger the variance, the more drastic the speed change and the more unstable it is. It indicates the maximum value of the speed sudden change of all wave peaks in the uniform speed running stage. The larger the value, the greater the speed change in a short time, and the more unstable it is. The larger the value, the more unstable the speed of the train will be. The larger it is, the greater the possibility of problems with the rail train, and the more attention it needs.

[0054] Step S313: Calculate the initial adjustment factor of the attention degree in the uniform speed running stage, and the corresponding calculation formula is: in, represents the initial adjustment factor of attention; Indicates speed instability; It represents the mean value of all cruising speeds during the uniform speed operation stage; Indicates the predetermined cruising speed during the uniform speed operation phase.

[0055] It can be explained that The larger it is, the greater the possibility that there will be problems with the rail train and the more attention it needs.

[0056] Step S314: determine the track train position coordinates at the start and end times of the uniform speed operation phase, construct a speed standard sequence, and determine the second attention level of the uniform speed operation phase.

[0057] It can be understood that the determination of the above values ​​indicates that there may be problems with the current rail train. There are many reasons for the speed fluctuations of the rail train during the uniform speed operation stage. Among them, the basic conditions of the track, such as changes in the slope and curvature of the track, lead to speed fluctuations, which is a normal phenomenon; mechanical failures, such as failures in the traction system, braking system or suspension system may lead to speed instability; changes in track conditions, such as obstacles on the track and track damage may affect the speed, so it is necessary to continue to analyze whether the speed fluctuations are normal or caused by problems, so the data is analyzed to determine the second level of attention.

[0058] Furthermore, in step S314, the calculation formula of the second attention degree is: in, Indicates the second level of attention; represents a nonstandard factor; Indicates the sequence composed of all sequence sub-segments in the uniform speed running stage; Indicates the speed standard sequence; express and Dynamic time warping distance of represents the linear normalization function.

[0059] Specifically, following the above, the target shift is used for explanation, and the rail train position coordinates at the start and end times of any uniform speed running subsegment from station one to station two of the target shift are used as the start and end target coordinates. In the standard time series of each dimension of the rail train from station one to station two, the speed standard sequence from the start target coordinate to the end target coordinate is obtained, wherein the track path of the sequence subsegment and its corresponding speed standard sequence is the same, that is, the basic conditions of the track are the same. The less similar the sequence subsegment is to the corresponding speed standard sequence, the more it indicates speed fluctuation in the uniform speed running stage, which is most likely a fault phenomenon.

[0060] It can be explained that express and The larger the value, the more dissimilar the uniform speed running stage is to its corresponding speed standard sequence. The more credible.

[0061] Furthermore, in step S3, the first attention level and the second attention level are combined to construct an attention level sequence of the target shift from the starting point to the end point, and multiple optimal time periods and corresponding attention levels are obtained, including: Step S321: determining the attention level of each initial period based on the first attention level and the second attention level, and constructing the attention levels of all initial periods into an attention level sequence from the starting point to the end point of the target shift; Step S322: Divide the attention sequence into multiple sequence segments by clustering, merge all initial time periods corresponding to each sequence segment to form each optimal time period, calculate the average of the attention of all initial time periods in the corresponding sequence segment, and determine the attention of each optimal time period.

[0062] Specifically, based on the above calculation, all the attentions of any train in each initial time period between any two stations are obtained in the same way, and then all the attentions of the target train from the starting station to the ending station are obtained, and all the obtained attentions are constructed into an attention sequence from the starting point to the end point; the K-means clustering algorithm is used to divide the attention sequence into multiple sequence segments, and all the initial time periods corresponding to each sequence segment are merged to form each optimal time period, and the mean of the attention of all the initial time periods in the corresponding sequence segment is calculated, and it is used as the attention of each optimal time period.

[0063] It can be explained that in step S4, all dimensional time series data corresponding to the optimal time period are encoded in parallel, and the attention degree corresponding to the optimal time period is used as a label to complete data integration.

[0064] Specifically, block Huffman coding is used to encode all train operation data in each optimal time period, that is, all dimensional data in parallel, to obtain a compressed code set corresponding to each optimal time period of each train, and the attention degree of each optimal time period is used as the label of its compressed code set; then the compressed code sets and labels of all optimal time periods of each train are stored in a database to complete data integration; wherein, block Huffman coding is to divide the data into multiple small blocks and then perform Huffman coding on each small block independently to achieve efficient compression effect. It is a variable-length coding method that assigns codes of different lengths to each character according to the frequency of occurrence in the data. Characters with high frequency use shorter codes, and characters with low frequency use longer codes.

[0065] It can be understood that by concentrating data of different levels of attention in one optimal time period and adding an identification label to the compressed code set of each optimal time period, data retrieval can be simplified to quickly locate the optimal time period containing the data of interest, so that when the subsequent analysis of train operation problems of each train is carried out, each optimal time period can be decoded in sequence from large to small according to the size of the label until the cause of the problem is analyzed, and the subsequent optimal time periods will no longer be decoded, thereby improving the effect of problem analysis.

[0066] The data integration method for rail train fusion host refers to a method of integrating and uniformly processing data from different systems and equipment on rail trains. Through data integration, data sharing, analysis and application can be realized to improve the safety, efficiency and service quality of train operation. It determines the weights between different train shifts by collecting data, analyzes the data to determine the optimal time period of the shift, and uses block Huffman coding based on the optimal time period to encode the data of each time period in parallel, thereby improving the coding efficiency, realizing segmented compression, and further improving the compression efficiency. This allows subsequent rail train operation problem analysis to only require decoding of the data of the optimal time period of the problem, thereby improving the processing efficiency of the integrated data.

[0067] It should be noted that the sequence of the above embodiments of the present invention is only for description and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0068] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

Claims

1. A data integration method for a rail train fusion host, characterized in that: The method comprises: Collect multi-dimensional time series data of rail train operation and perform preprocessing; Define any train in the rail train as the target train, determine the target dimension between the target train and the non-target train based on the preprocessed multi-dimensional time series data, obtain the corresponding specificity through the target dimension, and construct the target dimension set to obtain the weight of each dimension of the target train; analyze the dissimilarity between the target train and the non-target train, and determine the non-standard factor of the target train; The operation process of a rail train includes multiple initial time periods. The first attention level and the second attention level of each initial time period are determined based on non-standard factors and multi-dimensional time series data. The attention level sequence of the target train from the starting point to the end point is constructed by combining the first attention level and the second attention level, and multiple optimal time periods and corresponding attention levels are obtained. All dimensional time series data corresponding to the optimal period are encoded in parallel, and the attention level corresponding to the optimal period is used as a label to complete data integration; Define any train in the rail train as the target train, determine the target dimension between the target train and the non-target train based on the preprocessed multi-dimensional time series data, obtain the corresponding specificity through the target dimension, and construct the target dimension set to obtain the weight of each dimension of the target train, including: Define two adjacent stations as Station 1 and Station 2, obtain the time series of each dimension from Station 1 to Station 2 based on the target shift and any non-target shift, calculate the dynamic time warping distance of the time series of the same dimension, and sort them from large to small. The dimension corresponding to the largest dynamic time warping distance is the target dimension, and the target dimension corresponding to the target shift and each non-target shift is obtained; Calculate the specificity corresponding to each target dimension, build a target dimension set, count the number of times each identical target dimension appears, calculate the weight corresponding to each identical target dimension, assign the weight of the dimension in the non-target dimension set, and obtain the weight of each dimension of the target shift.

2. A data integration method for a rail train fusion host as claimed in claim 1, characterized in that: The multi-dimensional time series data of the rail train during operation is collected and preprocessed, wherein the multi-dimensional time series data includes any data of the speed, acceleration, train motor temperature, brake cylinder pressure, vibration data time series sequence and real-time position coordinates from the starting point to the end point, and the preprocessing method is standardized processing.

3. A data integration method for a rail train fusion host as claimed in claim 1, characterized in that: The calculation formula for the specificity of each target dimension is: in, Indicates the specificity of the target dimension; Indicates the dynamic time warping distance corresponding to the target dimension; Represents the mean of the dynamic time warping distances of all non-target dimensions.

4. A data integration method for a rail train fusion host as claimed in claim 1, characterized in that: The calculation formula for the weight corresponding to each same target dimension is: in, Indicates the target dimension set The weights of the same target dimension; Indicates the total number of target dimensions in the target dimension set; Indicates the first The total number of the same target dimensions; Indicates the first The maximum value of the specificity of all target dimensions in the same target dimension; represents the linear normalization function.

5. The data integration method for rail train fusion host according to claim 1, characterized in that: Analyze the dissimilarity between target shifts and non-target shifts and determine the non-standard factors of the target shifts, including: Calculate the dissimilarity between the target shift and each non-target shift from station one to station two to determine the cluster distance; All train schedules are divided into multiple clusters based on clustering distance, and the cluster with the largest number of schedules is defined as the standard cluster. The clustering distance between the target schedule and the cluster center of the standard cluster is obtained to obtain the non-standard factor of the target schedule.

6. A data integration method for a rail train fusion host as claimed in claim 5, characterized in that: The formula for calculating dissimilarity is: in, Indicates dissimilarity; Represents the total number of different dimensions in all dimensions; Indicates the target shift from station 1 to station 2. The weight of each dimension; Indicates the non-target shift from station 1 to station 2. The weight of each dimension; Indicates the target shift and non-target shift from station one to station two. Dynamic time warping distance of time series in one dimension.

7. A data integration method for a rail train fusion host as claimed in claim 5, characterized in that: The rail train operation process includes multiple initial periods. The first attention level and the second attention level of each initial period are determined based on non-standard factors and multi-dimensional time series data, including: The initial period includes the start-up acceleration stage, the uniform speed running stage and the deceleration braking stage during the operation of the rail train. The first attention level of the target train in the start-up acceleration stage and the deceleration braking stage is determined by the non-standard factor. Nonstandard factors; The uniform speed running stage is divided into several small sequence segments according to the duration, and the peaks and troughs of each small sequence segment in the uniform speed running stage are obtained to calculate the speed instability corresponding to the uniform speed running stage. The corresponding calculation formula is: in, Indicates speed instability; represents the variance of all speeds during the uniform speed running stage; It indicates the maximum value of the sudden change of speed of all wave crests in the uniform speed running stage; Indicates the duration of the uniform speed running phase; Indicates the total number of wave peaks in the uniform speed running stage; Calculate the initial adjustment factor of attention in the uniform speed running stage. The corresponding calculation formula is: in, represents the initial adjustment factor of attention; Indicates speed instability; It represents the mean value of all cruising speeds during the uniform speed operation stage; Indicates the cruise speed scheduled during the uniform speed operation phase; Determine the rail train position coordinates at the start and end times of the uniform speed operation phase, construct a speed standard sequence, and determine the second level of attention in the uniform speed operation phase.

8. A data integration method for a rail train fusion host as claimed in claim 7, characterized in that: The calculation formula for the second attention is: in, Indicates the second level of attention; represents a nonstandard factor; Indicates the sequence composed of all sequence sub-segments in the uniform speed running stage; Indicates the speed standard sequence; express and Dynamic time warping distance of represents the linear normalization function.

9. A data integration method for a rail train fusion host as claimed in claim 7, characterized in that: Combining the first attention and the second attention, the attention sequence of the target shift from the starting point to the end point is constructed to obtain multiple optimal time periods and corresponding attention, including: Determine the attention level of each initial period based on the first attention level and the second attention level, and construct the attention levels of all initial periods into an attention level sequence from the starting point to the end point of the target shift; The attention sequence is divided into multiple sequence segments by clustering, all initial time periods corresponding to each sequence segment are merged to form each optimal time period, the mean of the attention of all initial time periods in the corresponding sequence segment is calculated, and the attention of each optimal time period is determined.

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