Method and system for calculating the carrying capacity of train communication network services

Abstract

The application provides a train communication network service bearing capacity calculation method and system, belongs to the train communication network traffic management technical field, establishes a TRDP data throughput model and other protocol data throughput model; based on the established TRDP data throughput model and other protocol data throughput model, a total throughput model of the train communication network is established, and the network service bearing capacity is calculated and evaluated. The application models the TRDP multi-service flow based on the train communication network, including the arrival time interval, frequency and throughput model of the TRDP process data, and the throughput model of other protocol small flow data, finally establishes a network total throughput model, the established total throughput model better simulates the network throughput condition under the real train network environment, and can reserve bandwidth in advance according to the composition of the network service type, so that the network performance is ensured.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of train communication network traffic management, and particularly relates to a train communication network traffic carrying capacity calculation method and system. BACKGROUND

[0002] The train communication network (TCN) based on the switched Ethernet technology can be divided into two levels of architecture of the train level network (ETB) and the vehicle level network (ECN). The train network control system (TCMS) connects the subsystems in the whole train range with the TCN as the center, realizes train data information exchange and centralized control, and the train real time data protocol (TRDP) is used as a train special function control protocol to guarantee real time communication of the TCMS data. The protocol is mainly applied to transmission of two kinds of real time communication data types of process data and message data in the train communication network. However, with the gradual increase of the number of train on-board devices and network carrying services, different degrees of time delay jitter are generated in the transmission process of various service flows, which affects the cycle characteristics of the TRDP service and causes flow unpredictability. Other protocol flows for network control overhead also increase the network instantaneous throughput, and increase the difficulty of network congestion prediction.

[0003] At present, the TCN is mainly built by using wired technology, for example, the TCN based on the switched Ethernet technology involved in the present application scheme. The next generation of train communication network will gradually develop from wired to wireless, and will carry more train services in the future. Therefore, analysis and research on the train communication network flow composition and throughput can effectively promote the design of the future train communication network, and analysis of the flow type of the existing train network system, establishment of a reasonable flow model and flow prediction are important means to improve the network performance. The existing standard only defines the flow characteristics that the process data is real time periodic transmission and the message data is non-periodic transmission, but at present, there is a lack of train communication network flow analysis and model research based on measured data, and the train communication network performance and network multi-service carrying capacity cannot be targetedly improved. SUMMARY

[0004] The present application aims to provide a train communication network service carrying capacity calculation method and system, in the train communication network based on Ethernet technology, through building a test environment to capture and process the measured train network data, referring to the standard specified train communication network TRDP traffic characteristics, studying the TRDP traffic, other protocol traffic and network final throughput model under the train level and vehicle level network architecture, verifying the network multi-service carrying capacity, making the premise for subsequent effective improvement of the performance of the train communication network, to solve at least one technical problem in the above background technology.

[0005] In order to achieve the above-mentioned purpose, the present application adopts the following technical scheme:

[0006] In a first aspect, the present application provides a train communication network service carrying capacity calculation method, comprising:

[0007] Based on the train communication network topology structure and adding services, network traffic information is collected on the train level and vehicle level side;

[0008] The train level and vehicle level side collected network traffic information message data is preliminarily processed, and the performance indicators of the process data transmission are analyzed; according to the periodic service characteristics of the train communication network carrying service, the time delay jitter of each service transmission is modeled by using the random process, and the transmission cycle random model of the service is obtained;

[0009] Based on the distribution model of the arrival time interval and the arrival frequency of the process data, a TRDP data throughput model is established; according to the actual collected train network data classification result, combined with the traffic characteristics of each type of train network data, an other protocol data throughput model for network control overhead is established;

[0010] Based on the established TRDP data throughput model and other protocol data throughput model, a total throughput model of the train communication network is established, and the network service carrying capacity is calculated and evaluated.

[0011] Further, based on the train communication network topology structure and adding services, network traffic information is collected on the train level and vehicle level side, including the data acquisition service characteristic information, including the TRDP process data service communication identifier, period, data packet length; by screening data packets with the same ComId, source address and destination address, the transmission period of each TRDP service is obtained; the physical layer data frame length of the message is read and taken as the data packet length.

[0012] Further, the preliminary processing collects network traffic information message data at the train level and vehicle level, analyzes performance indicators of the process data transmission, models the time delay jitter of each item of business transmission according to the periodic business characteristics of the train communication network bearing business, obtains a transmission cycle random model of the business, including: assuming that the data packet carries a time stamp when arriving, the arrival time interval is the time difference of the continuous arrival message, obtaining the difference between the arrival times of two adjacent data packets, modeling the arrival time interval of the process data packet as a normal distribution with a mean of 100.002 ms and a variance of 0.127 ms, and modeling the arrival frequency of the process data packet as a normal distribution based on the established arrival time interval model.

[0013] Further, based on the distribution model of the arrival time interval and the arrival frequency of the process data, a TRDP data throughput model is established, including: based on the arrival time interval model and the distribution model of the arrival frequency of the process data, the throughput A of the process data is obtained, and the throughput model of the process data is determined by the frequency distribution, for the process data of ComId=1441, the throughput model thereof is established as a normal distribution with a mean of 14.559 kbps and a variance of 0.023 kbps.

[0014] Further, according to the classification results of the actually collected train network data, and in combination with the traffic characteristics of each type of train network data, other protocol data throughput models for network control overhead are established, including: the transmission characteristics of the ARP data packet are non-periodic transmission and non-fixed packet length, for the frequency model, the number of ARP data packets in a unit of time is approximately estimated to estimate the frequency distribution; the transmission characteristics of the EthernetII protocol data are periodic transmission and fixed packet length, the total throughput model of the EthernetII protocol traffic is modeled as a normal distribution with a mean of 6.399 kbps and a variance of 19.593 bps; the transmission characteristics of the IGMP data are non-periodic transmission and non-fixed packet length, the average frequency of the IGMP data packet in each burst transmission within 3s is respectively represented as The throughput A of the IGMP business is modeled as a periodic burst business model. g

[0015] In a second aspect, the present application provides a train communication network business bearing capacity calculation system, including:

[0016] The acquisition module is used for collecting network traffic information at the train level and vehicle level based on the train communication network topology structure and adding business;

[0017] ​The first modeling module is used for preliminarily processing train-level and vehicle-level side-collected network flow information message data, and analyzing performance indexes of process data transmission; according to periodic service characteristics of train communication network bearing services, time delay jitter of each service transmission is modeled by using a random process, and a transmission cycle random model of the service is obtained;

[0018] The second modeling module is used for establishing a TRDP data throughput model based on a distribution model of an arrival time interval and an arrival frequency of process data; according to a classification result of actually collected train network data, in combination with flow characteristics of each type of train network data, other protocol data throughput models for network control overhead are established;

[0019] The third calculation module is used for establishing a total throughput model of the train communication network based on the established TRDP data throughput model and the other protocol data throughput models, and calculating and evaluating network service bearing capacity.

[0020] In a third aspect, the present application provides a non-transitory computer readable storage medium for storing computer instructions, which, when executed by a processor, implement the train communication network service bearing capacity calculation method according to the first aspect.

[0021] In a fourth aspect, the present application provides a computer device including a memory and a processor, the processor and the memory being in communication with each other, the memory storing program instructions executable by the processor, and the processor invoking the program instructions to execute the train communication network service bearing capacity calculation method according to the first aspect.

[0022] In a fifth aspect, the present application provides an electronic device including a processor, a memory and a computer program; wherein the processor is connected with the memory, and the computer program is stored in the memory; when the electronic device is running, the processor executes the computer program stored in the memory, so that the electronic device executes instructions for implementing the train communication network service bearing capacity calculation method according to the first aspect.

[0023] Advantages of the present application: the TRDP multi-service flow based on the train communication network is modeled, including arrival time interval, frequency and throughput model of TRDP process data, and throughput model of other protocol small flow data, and finally a total throughput model of the network is established; the established total throughput model better simulates network throughput in a real train network environment, and can reserve bandwidth in advance according to a composition of service types in the network, so as to guarantee network performance.

[0024] Advantages of additional aspects of the present application will be more obviously given in the following description part, or be understood through practice of the present application. BRIEF DESCRIPTION OF DRAWINGS

[0025] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiments description. Obviously, the drawings in the following description only some embodiments of the present application, and for those skilled in the art, on the premise of not creating labor, can also obtain other drawings according to these drawings.

[0026] Figure 1 The TRDP multi-service traffic modeling method based on train communication network for the embodiments of the present application is shown in the flow chart.

[0027] Figure 2 The arrival time interval probability distribution and fitting results of process data for the embodiments of the present application are shown in the schematic diagram.

[0028] Figure 3 The network total throughput distribution curve under the measured data for the embodiments of the present application is shown in the graph.

[0029] Figure 4 The network total throughput distribution curve under the simulation model for the embodiments of the present application is shown in the graph.

[0030] Figure 5 The network throughput PDF comparison results of measured data and simulation model for the embodiments of the present application are shown in the schematic diagram. DETAILED DESCRIPTION

[0031] The embodiments of the present application will be described in detail below, and the examples of the embodiments are shown in the drawings, wherein the same or similar reference signs represent the same or similar elements or elements having the same or similar functions throughout. The embodiments described below through the drawings are exemplary and are only used to explain the present application, and cannot be explained as the limitation of the present application.

[0032] Those skilled in the art can understand that, unless otherwise defined, all terms (including technical terms and scientific terms) used herein have the same meaning as that generally understood by those skilled in the art in the field of the present application.

[0033] It should also be understood that terms such as those defined in general dictionaries should be understood as having meanings consistent with those in the prior art, and will not be interpreted with idealized or overly formal meanings unless defined as such.

[0034] It is to be understood that the singular forms "a," "an," and "the" include plural referents unless the context clearly dictates otherwise. It is further understood that the terms "comprising," "including," "containing," and "having" and the like, when used in the specification, specify the presence of stated features, integers, steps, operations, elements, and / or components but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof.

[0035] In the description of the present specification, the description of the terms "one embodiment", "some embodiments", "an example", "a specific example", or "some examples" and the like means that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present application. Moreover, the specific features, structures, materials or characteristics described can be combined in any appropriate manner in any one or more embodiments or examples. The person skilled in the art can combine and combine the different embodiments or examples described in the specification and the features of the different embodiments or examples, without contradiction.

[0036] In order to facilitate the understanding of the present application, the present application will be further explained and described in specific embodiments in conjunction with the accompanying drawings, and the specific embodiments do not constitute a limitation on the embodiments of the present application.

[0037] Those skilled in the art should understand that the drawings are only schematic views of the embodiments, and the components in the drawings are not necessarily essential for the implementation of the present application.

[0038] Embodiment 1

[0039] In this embodiment 1, first provide a train communication network service carrying capacity calculation system, comprising: acquisition module, for collecting network traffic information on train level, vehicle level side based on train communication network topology and adding service; first modeling module, for preliminary processing of train level, vehicle level side collected network traffic information packet data, analyzing performance indicators of process data transmission; according to the periodic service characteristics of train communication network carrying service, the time delay jitter of each service transmission is modeled by using random process, and the transmission cycle random model of the service is obtained; second modeling module, for establishing TRDP data throughput model based on the distribution model of the arrival time interval and arrival frequency of process data; according to the actual collected train network data classification result, combining the traffic characteristics of each type of train network data, other protocol data throughput model for network control overhead is established; third calculation module, for establishing the total throughput model of train communication network based on the established TRDP data throughput model and other protocol data throughput model, and calculating and evaluating the network service carrying capacity.

[0040] In this embodiment, the system is used to realize a train communication network service carrying capacity calculation method, which comprises the following steps: collecting network traffic information at the train level and the vehicle level based on the train communication network topology and adding services; preliminarily processing the network traffic information message data collected at the train level and the vehicle level, and analyzing the performance indicators of the process data transmission; modeling the delay jitter of each service transmission according to the periodic service characteristics of the train communication network service, obtaining the transmission cycle random model of the service; establishing a TRDP data throughput model based on the distribution model of the arrival time interval and the arrival frequency of the process data; establishing other protocol data throughput models for network control overhead according to the classification results of the actually collected train network data and combining the traffic characteristics of each type of train network data; establishing a total throughput model of the train communication network based on the established TRDP data throughput model and other protocol data throughput models, and calculating and evaluating the network service carrying capacity.

[0041] The network traffic information is collected at the train level and the vehicle level based on the train communication network topology and adding services, which comprises the following steps: collecting data to obtain service characteristic information, including the communication identifier, cycle and packet length of the TRDP process data service; obtaining the transmission cycle of each type of TRDP service by screening data packets with the same ComId, source address and destination address; reading the physical layer data frame length of the message and taking it as the data packet length.

[0042] The network traffic information message data collected at the train level and the vehicle level is preliminarily processed, the performance indicators of the process data transmission are analyzed, the delay jitter of each service transmission is modeled according to the periodic service characteristics of the train communication network service, and the transmission cycle random model of the service is obtained, which comprises the following steps: assuming that the data packet carries a time stamp when it arrives, the arrival time interval is the time difference between consecutive arrival messages, the difference between the arrival times of adjacent two data packets is obtained, the arrival time interval of the process data packet is modeled as a normal distribution with a mean of 100.002 ms and a variance of 0.127 ms, and the arrival frequency of the process data packet is modeled as a normal distribution based on the established arrival time interval model.

[0043] The TRDP data throughput model is established based on the distribution model of the arrival time interval and the arrival frequency of the process data, which comprises the following steps: obtaining the throughput A of the process data based on the arrival time interval model and the distribution model of the arrival frequency of the process data, the throughput model of the process data is determined by the frequency distribution, and for the process data with ComId=1441, the throughput model thereof is established as a normal distribution with a mean of 14.559 kbps and a variance of 0.023 kbps.

[0044] According to the actual collected train network data classification results, combined with the traffic characteristics of various types of train network data, other protocol data throughput models for network control overhead are established, including: the transmission characteristics of ARP data packets are non-periodic transmission and non-fixed packet length. For the frequency model, the number of ARP data packets in a unit of time is approximately estimated to estimate its frequency distribution; the transmission characteristics of EthernetII protocol data are periodic transmission and fixed packet length. The total throughput model of EthernetII protocol traffic is modeled as a normal distribution with a mean of 6.399kbps and a variance of 19.593bps; the transmission characteristics of IGMP data are non-periodic transmission and non-fixed packet length. The average frequency of IGMP data packets in each burst transmission within 3s is represented as The throughput A of IGMP service is modeled as a periodic burst service model. g

[0045] The transmission characteristics of IGMP data are non-periodic transmission and non-fixed packet length. The packet length of IGMP data has two cases of 64 and 60 bytes. The IGMP data with a period of 20s and a packet length of S g 60 bytes is transmitted in bursts. The duration of each burst transmission is 3s. The average frequency of IGMP data packets in each burst transmission within 3s is represented as The throughput A of IGMP service is modeled as a periodic burst service model. At the s-th second, the throughput of IGMP data is g

[0046]

[0047] Embodiment 2

[0048] As shown in Figures 1 to 5 In this embodiment, a TRDP multi-service traffic modeling method based on a train communication network is provided, as shown in Figure 1 The method comprises the following steps:

[0049] (S1) Establishing a train communication network topology and adding services, and collecting network traffic information at the train level and the vehicle level.

[0050] The service characteristic information is obtained through the collected data, mainly including the communication identifier, period and packet length of the TRDP process data service. For TRDP process data, the communication identifier (ComId) specific to different services can be read through Wireshark software. By filtering data packets with the same ComId, source address and destination address, the transmission period of each type of TRDP service can be obtained. The physical layer data frame length of the message is read and taken as the data packet length.

[0051] ​​(ii) Preliminary processing of message data, analysis of relevant performance indicators.

[0052] The measured data is preliminarily processed and divided into two parts: TRDP traffic and small traffic under other data protocols. In this embodiment, the measured data collected from a static train is of the process data (PD) type. Through analysis, it is known that the process data packets account for 97.27%, followed by Ethernet II protocol data, address resolution protocol (ARP) data and internet group management protocol (IGMP) data. The above traffic is modeled in this scheme, and a reasonable model is established by analyzing its own characteristics.

[0053] For process data, the transmission characteristics are periodicity of arrival time interval and fixed packet length. Each TRDP data packet header contains ComId information, and process data with the same ComId and characteristic period is called group data. The vehicle-level network undertakes the message transmission task of more than 60 on-board subsystems, and the transmission periods of the messages of various subsystems are designed to be different. Considering that the transmission characteristics of each type of process data are similar, in this embodiment, the process data with ComId 1441 and transmission period 100 ms is taken as an example to establish a TRDP data transmission model. The process data packets are not generated completely periodically, and each transmission may have different degrees of jitter. Through analysis, the performance indicators of the process data transmission with ComId 1441 are as follows: the packet loss rate is 0, the mean and variance of the arrival time interval of the data packets are 100.008 ms and 0.159 ms respectively, and the maximum jitter is 0.897 ms. Therefore, the transmission jitter makes the data packet transmission period not strictly meet 100 ms, resulting in the final network throughput belonging to an unknown random process.

[0054] (iii) According to the periodic service characteristics of the train communication network carrying services, the transmission delay jitter of each service is modeled using a random process to obtain a random model of the transmission period of the service.

[0055] Considering that different transmission periods have no effect on the jitter characteristics of different types of process data, in this embodiment, the process data with ComId 1441 is taken as an example, and the arrival time interval and frequency model of this type of data is established through data preprocessing. Similarly, the models of other types of process data are established.

[0056] It is assumed that the data packet arrives with a timestamp T a , and the arrival time interval T i is the time difference between consecutive messages, i.e. the difference between the arrival times of two adjacent data packets: T i = T a [n+1]-T a [n].

[0057] Figure 2 For the frequency distribution histogram of the process data packet arrival time interval and its fitting results, according to the distribution characteristics, the arrival time interval of the data is fitted by using known distribution models such as normal, Weibull, Cauchy and the like. In order to evaluate the fitting, in this embodiment, Akaike information criterion (AIC) and root mean square error (RMSE) are selected as two evaluation indexes to evaluate the model fitting results. The smaller the values of the above two evaluation indexes are, the higher the model accuracy is. The evaluation results show that the normal distribution has better fitting accuracy, as shown in Table 1:

[0058] Table 1 Fitting results of process data arrival time interval distribution

[0059]

[0060] Therefore, in this embodiment, the arrival time interval of the process data packet with ComId 1441 is modeled as a normal distribution with a mean of 100.002 ms and a variance of 0.127 ms, and its PDF is:

[0061]

[0062] Based on the above established arrival time interval model, a process data packet frequency distribution model is established in this embodiment. Let the process data packet frequency expression be To derive the model of the frequency distribution, consider that the variable X obeys a non-standard normal distribution, and let The variable Y can be obtained as and x= The PDF of the variable Y can be expressed as:

[0063]

[0064] To verify by example, assume that X obeys a normal distribution with a mean of 10 and a variance of 0.1. Let y1=x, The above two curves are fitted by using a normal distribution, and the fitting evaluation index RMSE is used to evaluate the fitting of y2. The fitting result of RMSE is 1.109, indicating that the normal distribution has a good fitting effect on the frequency distribution F i .

[0065] In summary, the arrival frequency of the process data packet is modeled as a normal distribution. The frequency distribution of the process data packet with ComId 1441 obeys a normal distribution with a mean of 9.999 Hz and a variance of 0.011 Hz.

[0066] (Four) Establish a TRDP data throughput model.

[0067] Based on the arrival time interval Ti and the distribution model of arrival frequency F i The throughput A of the data can be expressed as: where S i is the packet length of the process data, i.e. the frame length of the physical layer data frame of the process data packet (unit: byte).

[0068] Considering that the process data packet has a fixed packet length, according to the above formula, the throughput model of the process data is determined by the frequency distribution. For the process data of ComId = 1441, the established throughput model is a normal distribution, and the mean is 14.559 kbps and the variance is 0.023 kbps.

[0069] Since the various services in the train network are independently transmitted, there is no correlation between the services, and the sum of multiple independent normal distributions still obeys a normal distribution. Therefore, the total throughput model of the process data is established as a normal distribution model. The partial fitting parameter results of the throughput models of different process data services are shown in Table 2.

[0070] Table 2

[0071]

[0072] In summary, the total throughput of all process data collected by the train vehicle level network in the embodiment is modeled as a normal distribution model with a mean of 6.299 Mbps and a variance of 14.828 kbps.

[0073] (Five) Combined with its own traffic characteristics, other protocol data throughput models for network control overhead are established.

[0074] According to the classification results of the actually collected train network data, there are other protocol data in addition to the TRDP data in the train communication data, such as EthernetII protocol, ARP and IGMP, etc., which together realize the maintenance of the train network and system. For example, ARP converts the IP address of the data packet into a physical address, and IGMP establishes and maintains the members of the multicast group. In this embodiment, from the perspective of service modeling, such data packets are divided into small flow data, and together with the TRDP data, they constitute the train communication network data. For the data flow model of the small flow protocol, the EthernetII protocol, ARP and IGMP flow models are mainly considered, which account for 1.16%, 0.81% and 0.75% respectively in the train communication network data. The data packet sending times of other protocols are less, and the proportion is too low, so they are temporarily ignored.

[0075] The transmission characteristics of ARP data packets are non-periodic transmission and non-fixed packet length. The number of ARP data packets in a unit of time is used to estimate the frequency distribution of ARP data packets. In this embodiment, the frequency of ARP data packets is modeled as a Poisson distribution with a parameter λ = 13.879 Hz. ARP data packets have two packet lengths, 64 bytes and 60 bytes. The ARP data throughput formula is calculated using the mean packet length. Therefore, the total throughput of ARP data packets in the train vehicle network is modeled as a Poisson distribution.

[0076] The transmission characteristics of EthernetII protocol data are periodic transmission and fixed packet length. Therefore, the traffic model of this type of data is consistent with the process data, and the total throughput model of EthernetII protocol traffic is modeled as a normal distribution with a mean of 6.399 kbps and a variance of 19.593 bps.

[0077] The transmission characteristics of IGMP data are non-periodic transmission and non-fixed packet length. IGMP data packets have two packet lengths, 64 bytes and 60 bytes. About 95% of IGMP data packets are transmitted in bursts with a period of 20s and a packet length of 60 bytes. g The duration of each burst transmission is 3s. The average frequency of IGMP data packets in each burst transmission of 3s is represented as and the throughput A g of IGMP traffic is modeled as a periodic burst traffic model. In the s-th second, the throughput of IGMP data is

[0078]

[0079] (VI) Using the TRDP and other protocol data throughput models established in the above steps, the total throughput model of the train communication network is established, and the network traffic carrying capacity is evaluated.

[0080] Through data preprocessing, the main difference between the train backbone network and the train onboard network in terms of traffic composition is that the former has a specific type of process data with a ComId of 1001 for network topology discovery in the train backbone network. Since the data composition is similar, the traffic in the ECN is modeled and verified.

[0081] Based on the throughput models of TRDP data and other small traffic data under the above protocols, the total throughput model of the train communication network is researched and verified. The throughput distribution of the measured throughput data is shown in Figure 3 , with an average of 6.324 Mbps and random jitter. In addition, Figure 3 , it can be found that there are periodic peaks significantly higher than the average. Figure 4The results of the aggregate traffic generated by the traffic model established according to the present scheme are shown. Since the data transmission processes of various protocols are independent of each other, the overall throughput of the TCN can be described by the sum of several different traffic models. In order to illustrate the characteristics of the PD traffic and other small traffic, the throughput results of the ECN with only process data traffic and with all traffic are verified respectively. It can be seen from the results that the periodic peak is mainly caused by the IGMP traffic periodically generating a large number of data packets. Therefore, the small traffic has a non-negligible impact on the overall throughput in the TCN. The normalized PDF comparison results of the model are shown in Figure 5 The model can approximately calculate the carrying capacity of the train communication network traffic. For example, according to the verification results, the small traffic data packets of the train communication network will have different degrees of jitter and burst packet transmission, affecting the periodic characteristics of the TRDP data and the instantaneous network throughput of the network. By considering the composition of the traffic types in the network, bandwidth is reserved in advance to ensure network performance and avoid congestion.

[0082] In summary, in the present embodiment, the TRDP multi-service traffic based on the train communication network is modeled, including the arrival time interval, frequency and throughput model of the TRDP process data, and the throughput model of other protocol small traffic data, and finally the network total throughput model is established. The existing research lacks the analysis and model establishment of the train communication network traffic based on the measured data, and cannot improve the network performance in a targeted manner. The total throughput model established in the present embodiment better simulates the network throughput situation in the real train network environment, and can reserve bandwidth in advance according to the composition of the traffic types in the network to ensure network performance.

[0083] Embodiment 3

[0084] The present embodiment 3 provides a non-transitory computer readable storage medium for storing computer instructions, which, when executed by a processor, implements the train communication network traffic carrying capacity calculation method as described above, and the method comprises the following steps of:

[0085] Based on the train communication network topology structure and adding traffic, network traffic information is collected at the train level and the vehicle level;

[0086] The network traffic information message data collected at the train level and the vehicle level are preliminarily processed, and the performance indicators of the process data transmission are analyzed; according to the periodic traffic characteristics of the train communication network carrying traffic, the time delay jitter of each traffic transmission is modeled by using a random process, and a traffic transmission cycle random model is obtained;

[0087] Based on the distribution model of the arrival time interval and the arrival frequency of the process data, a TRDP data throughput model is established; according to the classified results of the actually collected train network data, in combination with the traffic characteristics of each type of train network data, other protocol data throughput models for network control overhead are established;

[0088] Based on the established TRDP data throughput model and the other protocol data throughput models, a total throughput model of the train communication network is established, and the network service carrying capacity is calculated and evaluated.

[0089] Embodiment 4

[0090] The embodiment 4 provides a computer device, comprising a memory and a processor, the processor and the memory are in communication with each other, the memory stores program instructions executable by the processor, and the processor invokes the program instructions to execute the train communication network service carrying capacity calculation method as described above, the method comprising:

[0091] Based on the train communication network topology structure and added services, network traffic information is collected at the train level and the vehicle level;

[0092] Preliminary processing of the network traffic information message data collected at the train level and the vehicle level, analysis of the performance indicators of the process data transmission; according to the periodic service characteristics of the train communication network service, the time delay jitter of each service transmission is modeled by using a random process, and a transmission cycle random model of the service is obtained;

[0093] Based on the distribution model of the arrival time interval and the arrival frequency of the process data, a TRDP data throughput model is established; according to the classified results of the actually collected train network data, in combination with the traffic characteristics of each type of train network data, other protocol data throughput models for network control overhead are established;

[0094] Based on the established TRDP data throughput model and the other protocol data throughput models, a total throughput model of the train communication network is established, and the network service carrying capacity is calculated and evaluated.

[0095] Embodiment 5

[0096] The embodiment 5 provides an electronic device, comprising a processor, a memory and a computer program; wherein the processor is connected with the memory, and the computer program is stored in the memory; when the electronic device is running, the processor executes the computer program stored in the memory, so that the electronic device executes the instructions for realizing the train communication network service carrying capacity calculation method as described above, and the method comprises:

[0097] Based on the train communication network topology structure and added services, network traffic information is collected at the train level and the vehicle level;

[0098] The preliminary processing collects network traffic information message data at the train level and vehicle level, and analyzes performance indexes of the process data transmission; according to the periodic service characteristics of the train communication network service, a random process is used to model the time delay jitter of each service transmission, and a service transmission cycle random model is obtained;

[0099] Based on the distribution model of the arrival time interval and the arrival frequency of the process data, a TRDP data throughput model is established; according to the classification results of the actually collected train network data, and in combination with the traffic characteristics of each type of train network data, other protocol data throughput models for network control overhead are established;

[0100] Based on the established TRDP data throughput model and the other protocol data throughput models, a total throughput model of the train communication network is established, and the network service carrying capacity is calculated and evaluated.

[0101] Those skilled in the art will understand that embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROMs, optical storage, etc.) containing computer-usable program code.

[0102] The present application is described with reference to flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of flows and / or blocks in the flowcharts and / or block diagrams can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing apparatus to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing apparatus produce an apparatus that implements the functions specified in the flowcharts and / or block diagrams. Figure 1 The functions specified in one or more flows and / or blocks. Figure 1 The apparatus that implements the functions specified in one or more flows and / or blocks.

[0103] These computer program instructions can also be stored in a computer-readable memory that can direct the computer or other programmable data processing apparatus to work in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including instruction apparatus that implements the functions specified in the flowcharts and / or block diagrams. Figure 1 The functions specified in one or more flows and / or blocks. Figure 1 The apparatus that implements the functions specified in one or more flows and / or blocks.

[0104] These computer program instructions can also be loaded into a computer or other programmable data processing devices, to cause a series of operational steps to be performed on the computer or other programmable data processing devices, so as to generate a computer implemented process, thus the instructions executed on the computer or other programmable data processing devices provide a process for implementing the functions specified in the flowchart Figure 1 one flow or multiple flows and / or the functions specified in the block Figure 1 Figure 1 one block or multiple blocks.

[0105] The above describes the specific embodiments of the present application in conjunction with the drawings, but is not a limitation on the scope of protection of the present application, and those skilled in the art should understand that various modifications or changes made by those skilled in the art on the basis of the technical solutions disclosed in the present application without creative labor should be covered within the scope of protection of the present application.

Claims

1. A method of calculating a carrying capacity of a train communication network service, characterized by, Comprise: Collect network traffic information at train level and vehicle level based on train communication network topology and adding services; Preliminary process train level and vehicle level network traffic information message data, analyze process data transmission performance indicators; according to the periodic service characteristics of train communication network bearing services, use random process to model the transmission delay jitter of each service, get the transmission cycle random model of the service; including: through data preprocessing, establish the arrival time interval and frequency model of process data; assume that the data packet carries a time stamp when it arrives, the arrival time interval is the time difference of continuous arrival message, that is, the difference between the arrival time of adjacent two data packets, fit the arrival time interval, select two evaluation indexes of Akaike information criterion and root mean square error to evaluate the fitting result, model the arrival time interval of process data packet as the distribution model with the minimum value of two evaluation indexes; based on the established arrival time interval model, establish the data packet arrival frequency distribution model of process data; wherein, the arrival time interval of process data packet is modeled as a normal distribution with a mean of 100.002ms and a variance of 0.127ms; the data packet arrival frequency distribution model of process data is modeled as a normal distribution with a mean of 9.999Hz and a variance of 0.011Hz; Based on the distribution model of the arrival time interval and the arrival frequency of the process data, a TRDP data throughput model is established; including: based on the distribution model of the arrival time interval T i and the arrival frequency F i of the process data, the throughput A of the data can be expressed as where S i is the packet length of the process data, i.e. the frame length of the physical layer data frame of the process data packet; then the TRDP data throughput model is determined by the arrival frequency distribution; the total throughput of all process data collected by the train vehicle level network is modeled as a normal distribution model with a mean of 6.299Mbps and a variance of 14.828kbps; according to the classification results of the actually collected train network data, combined with the traffic characteristics of each type of train network data, other protocol data throughput models for network control overhead are established; including: the transmission characteristics of ARP data packets are non-periodic transmission and non-fixed packet length, for the frequency model, the number of ARP data packets in a unit of time is approximately estimated to estimate its frequency distribution; the transmission characteristics of Ethernet II protocol data are periodic transmission and fixed packet length, the total throughput model of Ethernet II protocol traffic is modeled as a normal distribution with a mean of 6.399kbps and a variance of 19.593bps; the transmission characteristics of IGMP data are non-periodic transmission and non-fixed packet length, IGMP data are transmitted in bursts with a period of 20s and a packet length S g of 60 bytes, and the duration of each burst transmission is 3s; the average frequency of IGMP data packets in each burst transmission of 3s is expressed as Hz, and the throughput A g of IGMP service is modeled as a periodic burst service model; at the s-th second, the throughput of IGMP data is Based on the established TRDP data throughput model and other protocol data throughput model, the total throughput model of train communication network is established as the sum of different types of protocol data flow model, and the network service bearing capacity is calculated and evaluated.

2. The method of claim 1, wherein, Collect network traffic information at train level and vehicle level based on train communication network topology and adding services, including: the collected data obtain service characteristic information, including TRDP process data service communication identifier, period, data packet length; by screening data packets with the same ComId, source address and destination address, the transmission period of each TRDP service is obtained; read the physical layer data frame length of the message and take it as the data packet length.

3. A system for calculating the carrying capacity of a train communication network service, characterized in that Comprise: Collecting module, for collecting network traffic information at train level and vehicle level based on train communication network topology and adding services; The first modeling module is used for preliminarily processing train-level and vehicle-level side-acquired network traffic information message data, and analyzing performance indexes of process data transmission; according to periodic service characteristics of train communication network bearing services, time delay jitter of each service transmission is modeled by using a random process to obtain a transmission cycle random model of the services; the method comprises the following steps: an arrival time interval and a frequency model of process data are established through data preprocessing; it is assumed that a data packet carries a time stamp when it arrives, and the arrival time interval is the time difference of continuously arriving messages, that is, the difference between the arrival times of two adjacent data packets; the arrival time interval is fitted, and two evaluation indexes, Akaike information criterion and root mean square error, are selected to evaluate the fitting result; the arrival time interval of the process data packet is modeled as a distribution model with the minimum values of the two evaluation indexes; based on the established arrival time interval model, a data packet arrival frequency distribution model of the process data is established; wherein, the arrival time interval of the process data packet is modeled as a normal distribution with a mean value of 100.002 ms and a variance of 0.127 ms; the data packet arrival frequency distribution model of the process data is modeled as a normal distribution with a mean value of 9.999 Hz and a variance of 0.011 Hz; The second modeling module is used for establishing the TRDP data throughput model based on the distribution model of the arrival time interval and the arrival frequency of the process data; including: based on the distribution model of the arrival time interval T i and the arrival frequency F i of the process data, the throughput A of the data can be expressed as where S i is the packet length of the process data, i.e. the frame length of the physical layer data frame of the process data packet; then the TRDP data throughput model is determined by the arrival frequency distribution; the total throughput of all process data collected by the train vehicle level network is modeled as a normal distribution model with a mean of 6.299 Mbps and a variance of 14.828 kbps; according to the classification results of the actually collected train network data, combined with the traffic characteristics of each type of train network data, other protocol data throughput models for network control overhead are established; including: the transmission characteristics of ARP data packets are non-periodic transmission and non-fixed packet length, for the frequency model, the number of ARP data packets in a unit of time is approximately estimated to estimate the frequency distribution; the transmission characteristics of Ethernet II protocol data are periodic transmission and fixed packet length, the total throughput model of Ethernet II protocol traffic is modeled as a normal distribution with a mean of 6.399 kbps and a variance of 19.593 bps; the transmission characteristics of IGMP data are non-periodic transmission and non-fixed packet length, IGMP data are transmitted in bursts with a period of 20 s and a packet length S g of 60 bytes, and the duration of each burst transmission is 3 s; the average frequency of IGMP data packets in the 3 s time of each burst transmission is respectively expressed as Hz, and the throughput A g of IGMP service is modeled as a periodic burst service model; at the s-th second, the throughput of IGMP data is The third calculation module is used for establishing a total throughput model of the train communication network as a sum of different types of protocol data flow models based on the established TRDP data throughput model and other protocol data throughput models, and calculating and evaluating network service bearing capacity.

4. A non-transitory computer-readable storage medium, comprising, The non-transitory computer readable storage medium is used for storing computer instructions, and the computer instructions are executed by the processor to realize the train communication network service bearing capacity calculation method of claim 1 or 2.

5. A computer device, comprising: The electronic device comprises a memory and a processor, the processor and the memory are in communication with each other, the memory stores program instructions executable by the processor, and the processor invokes the program instructions to execute the train communication network service bearing capacity calculation method of claim 1 or 2.

6. An electronic device, comprising: The electronic device comprises: A processor, a memory and a computer program; wherein the processor is connected with the memory, the computer program is stored in the memory, and when the electronic device is running, the processor executes the computer program stored in the memory to make the electronic device execute instructions for realizing the train communication network service bearing capacity calculation method of claim 1 or 2.

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