Train speed measurement method and device

Abstract

The present specification provides a train speed measurement method and device, wherein the train speed measurement method comprises: obtaining historical running information of a target train in a historical running period and observed running information of the target train in a current running period, wherein the running information comprises running speed information and running acceleration information; generating initial running information of the target train in the current running period according to the historical running information; obtaining historical posterior error information of the target train in the historical running period, and calculating prior error information of the target train in the current running period according to the historical posterior error information; calculating an information fusion weight according to the prior error information; and fusing the initial running information and the observed running information according to the information fusion weight to obtain target running information of the target train in the current running period. Not only the error caused by the speed sensor in the target train is considered, but also the error caused by the accelerometer is considered, so that the accuracy and availability of the train speed measurement are improved.

Description

TECHNICAL FIELD

[0001] The present specification relates to the field of rail transit, in particular to a train speed measurement method. The present specification also relates to a train speed measurement device, a computing device, a computer readable storage medium and a computer program product. BACKGROUND

[0002] In the field of rail transit, the speed measurement function in the on-board ATP (Automatic Train Protection) system belongs to the basic function. In the current practical application, the speed information of the train is mostly collected by the speed sensor for calculation and measurement, but the speed information of the speed sensor often has errors in the calculation process, such as the error of the speed sensor itself, the noise in the process of collecting speed data, the error of the wheel diameter value, etc. Among these errors, some errors can be measured and calculated, and some errors cannot be measured and calculated. Therefore, the current measurement and calculation of train speed information is often the estimated value of the actual running speed of the train.

[0003] Currently, the industry mostly uses speed sensors and radars, or speed sensors and accelerometers to measure the speed of the train. The speed information of the speed sensor is usually used, and the values of the radar or the accelerometer are used to supervise the speed information of the speed sensor. However, in actual application, the accelerometer itself may also have errors. Therefore, measuring the speed of the train based on the above method will reduce the accuracy of train speed measurement. SUMMARY

[0004] Therefore, the embodiments of the present specification provide a train speed measurement method. One or more embodiments of the present specification also relate to a train speed measurement device, a computing device, a computer readable storage medium and a computer program product to solve the technical defects in the prior art.

[0005] According to a first aspect of the embodiments of the present specification, a train speed measurement method is provided, comprising:

[0006] obtaining historical running information of a target train in a historical running period and observed running information of the target train in a current running period, wherein the running information includes running speed information and running acceleration information;

[0007] generating initial running information of the target train in the current running period according to the historical running information;

[0008] obtaining historical posterior error information of the target train in the historical running period, and calculating prior error information of the target train in the current running period according to the historical posterior error information;

[0009] According to the prior error information, an information fusion weight is calculated;

[0010] According to the information fusion weight, the initial operation information and the observed operation information are fused to obtain target operation information of the target train in the current operation period.

[0011] According to a second aspect of the embodiments of the present specification, a train speed measurement device is provided, comprising:

[0012] An acquisition module is configured to acquire historical operation information of a target train in a historical operation period and observed operation information of the target train in a current operation period, wherein the operation information comprises operation speed information and operation acceleration information;

[0013] A generation module is configured to generate initial operation information of the target train in the current operation period according to the historical operation information;

[0014] A first calculation module is configured to acquire historical posterior error information of the target train in the historical operation period, and calculate prior error information of the target train in the current operation period according to the historical posterior error information;

[0015] A second calculation module is configured to calculate an information fusion weight according to the prior error information;

[0016] A fusion module is configured to fuse the initial operation information and the observed operation information according to the information fusion weight to obtain target operation information of the target train in the current operation period.

[0017] According to a third aspect of the embodiments of the present specification, a computing device is provided, comprising:

[0018] a memory and a processor;

[0019] The memory is configured to store computer programs / instructions, and the processor is configured to execute the computer programs / instructions, which realize the steps of the train speed measurement method described above.

[0020] According to a fourth aspect of the embodiments of the present specification, a computer readable storage medium is provided, which stores computer programs / instructions, which realize the steps of the train speed measurement method described above when executed by a processor.

[0021] According to a fifth aspect of the embodiments of the present specification, a computer program product is provided, comprising computer programs / instructions, which realize the steps of the train speed measurement method described above when executed by a processor.

[0022] An embodiment of the present specification realizes that, according to the running speed information and the running acceleration information of the target train, the speed of the target train is measured, and in the process of measuring the speed of the target train, not only the error caused by the speed sensor in the target train is considered, but also the error caused by the accelerometer is considered, so as to reduce the problem of low accuracy of measuring the speed of the train caused by the error, and improve the accuracy and availability of train speed measurement. BRIEF DESCRIPTION OF DRAWINGS

[0023] Figure 1 is a flowchart of a train speed measurement method provided by an embodiment of the present specification;

[0024] Figure 2a is a schematic diagram of interaction between an ATP system and various sensors provided by an embodiment of the present specification;

[0025] Figure 2b is a schematic diagram of obtaining target running information of a target train in a current running cycle provided by an embodiment of the present specification;

[0026] Figure 3 is a flowchart of a processing process of a train speed measurement method provided by an embodiment of the present specification;

[0027] Figure 4 is a structural schematic diagram of a train speed measurement device provided by an embodiment of the present specification;

[0028] Figure 5 is a structural block diagram of a computing device provided by an embodiment of the present specification. DETAILED DESCRIPTION

[0029] In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present specification. However, the present specification can be practiced without the specific details, other than those described herein, and it is understood that the present specification will encompass numerous implementations beyond those described herein. Accordingly, the present specification does not intend to limit the scope of the present specification to the related details described herein but intends to claim the full scope of the present specification, including all equivalents of the related details.

[0030] The terms used in one or more embodiments of the present specification are merely for the purpose of describing specific embodiments and are not intended to limit one or more embodiments of the present specification. The singular forms "a", "an" and "the" used in one or more embodiments of the present specification and the appended claims are also intended to include plural forms, unless the context clearly indicates otherwise. It should also be understood that the term "and / or" used in one or more embodiments of the present specification means and includes any or all possible combinations of one or more associated listed items.

[0031] It should be understood that, although the terms first, second, etc. can be employed in describing various information in one or more embodiments of the present specification, the information should not be limited to such terms. These terms are only used to differentiate one piece of information from another piece of information of the same type. For example, without departing from the scope of one or more embodiments of the present specification, first can also be referred to as second, and similarly, second can also be referred to as first. Depending on the context, the word "if' as used herein can be interpreted as meaning "when" or "upon determining" or "in response to determining".

[0032] In addition, it should be noted that the user information (including but not limited to user equipment information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in one or more embodiments of the present specification are all information and data authorized by the user or authorized by all parties, and the collection, use and processing of related data need to comply with relevant laws, regulations and standards of relevant countries and regions, and provide corresponding operation portal for user to choose authorization or refusal.

[0033] Firstly, the nomenclature involved in one or more embodiments of the present specification is explained.

[0034] ATP: Automatic Train Protection, train automatic protection system.

[0035] Kalman filtering: Kalman filtering is an algorithm that uses linear system state equations to make optimal estimates of system states based on system input and output observation data. Kalman filtering algorithm represents system states as linear stochastic processes and recursively estimates system states using observation data.

[0036] In the actual application of rail transit, the speed information of the train is usually collected by the speed sensor to calculate and measure the speed of the train. However, there are errors in the calculation process of the speed information of the speed sensor, such as the error of the speed sensor itself, the noise in the process of collecting speed data, the error of the wheel diameter value, etc. Among these errors, some errors can be measured and calculated, and some errors cannot be measured and calculated. Therefore, the current measurement and calculation of train speed information is usually an estimated value of the actual running speed of the train.

[0037] Currently, the industry mostly uses speed sensors and radars, or speed sensors and accelerometers to measure the speed of the train. The speed information of the speed sensor is usually used, and the value of the radar or the accelerometer is used to supervise the speed information of the speed sensor. However, in actual application, the accelerometer itself can also have errors. Therefore, based on the above method, the accuracy of train speed measurement will be reduced.

[0038] In the present specification, a train speed measurement method is provided, and the present specification also relates to a train speed measurement device, a computing device, a computer readable storage medium and a computer program product, which are described in detail one by one in the following embodiments.

[0039] Referring to Figure 1 , Figure 1 A flow chart of a train speed measurement method according to one embodiment of the present specification is shown, which specifically includes the following steps:

[0040] Step 102: obtaining historical running information of a target train in a historical running period and observed running information of the target train in a current running period, wherein the running information includes running speed information and running acceleration information.

[0041] In the actual application of rail transit, it is a basic function to measure the speed of a train based on an ATP system, but the speed information obtained based on a speed sensor is often subject to a large number of calculation errors in the calculation process, such as errors of the sensor itself, noise in the process of collecting speed information, etc., and these errors will reduce the accuracy of the train speed measurement. Therefore, how to further improve the accuracy of the train speed measurement is crucial.

[0042] The target train refers to any train in a train line, and the target train can be in a driving state, a starting state or a parking state. The actual running state of the target train can be determined according to the actual application, which is not limited in the present specification. The running period refers to the running period of the ATP system deployed in the target train; the current running period refers to the current running period of the ATP system; and the historical running period refers to the historical running period of the ATP system, specifically the previous running period of the current running period. For example, if the current running period is the kth running period, the historical running period is the (k-1)th running period.

[0043] The historical running information refers to a posteriori running information of the target train in a historical running period, and specifically includes historical running speed information and historical running acceleration information of the target train in the historical running period. Since two speed sensors are usually deployed in the target train, the historical running speed information includes first historical running speed information and second historical running speed information. The observed running information refers to running information of the target train in a current running period observed based on sensors of the target train, and specifically includes observed running speed information and observed running acceleration information of the target train in the current running period. The observed running speed information includes first observed running speed information and second observed running speed information. The first observed running speed information is obtained based on observation of a first speed sensor of the target train, the second observed running speed information is obtained based on observation of a second speed sensor of the target train, and the observed running acceleration information is obtained based on observation of an accelerometer of the target train.

[0044] Referring to Figure 2a , Figure 2a An interaction diagram of an ATP system and various sensors according to one embodiment of the present specification is shown. As shown in Figure 2a , two speed sensors and one accelerometer are deployed in the target train, and the two speed sensors are a first speed sensor and a second speed sensor. In actual application, after the first speed sensor, the second speed sensor and the accelerometer observe the running information of the target train, the running information is sent to the ATP system, and the ATP system processes the running information to realize speed measurement of the target train.

[0045] After the ATP system obtains the observed running information of the target train in the current running period, that is, the first observed running speed information, the second observed running speed information and the observed acceleration information, the historical running information of the target train in the historical running period can be obtained, that is, the first historical running speed information, the second historical running speed information and the historical running acceleration information. In order to reduce the influence of data errors on speed measurement in the calculation process, a Kalman filtering algorithm can be used to filter the historical running information and the observed running information to obtain filtered running information and improve the accuracy of train speed measurement.

[0046] Referring to Figure 2b , Figure 2b An interaction diagram of an ATP system and various sensors according to one embodiment of the present specification is shown. As shown in Figure 2bAs shown, the process of filtering the historical running information and the observed running information based on the Kalman filtering algorithm to obtain the filtered running information, i.e., the target running information, specifically includes two parts of time update and state update. The time update part specifically refers to predicting the predicted value of the target train in the current running period, i.e., the initial running information, according to the historical running information of the target train in the historical running period. The state update part specifically refers to estimating and correcting the running speed information and the running acceleration information of the target train according to the predicted value of the target train in the current running period, i.e., the initial running information, and the observed value of the target train in the current running period, i.e., the observed running information.

[0047] The target running information refers to the posterior running information of the target train in the current running period, specifically including the first target running speed information, the second target running speed information and the target running acceleration information of the target train in the current running period. The initial running information refers to the prior running information of the target train in the current running period, specifically including the first initial running speed information, the second initial running speed information and the initial running acceleration information of the target train in the current running period.

[0048] In one or more embodiments provided in the present specification, in the process of measuring the speed of the target train using the Kalman filtering algorithm, not only the running speed information of the target train is filtered, but also the running acceleration information of the target train is filtered, so as to reduce the error caused by the speed and acceleration in actual application and improve the accuracy of measuring the speed of the train.

[0049] Step 104: generating the initial running information of the target train in the current running period according to the historical running information.

[0050] After obtaining the historical running information of the target train in the historical running period, the prior running information, i.e., the initial running information, of the target train in the current running period can be predicted according to the historical running information.

[0051] In a specific embodiment provided in the present specification, generating the initial running information of the target train in the current running period according to the historical running information includes:

[0052] obtaining a preset state transition matrix;

[0053] generating the initial running information of the target train in the current running period according to the preset state transition matrix and the historical running information.

[0054] Specifically, after obtaining the historical running information of the target train in the historical running period, a preset state transition matrix is obtained, and the initial running information of the target train in the current running period is calculated according to the preset state transition matrix and the historical running information. The process of calculating the initial running information can be referred to formula 1 as follows:

[0055]

[0056] wherein, is the initial running information of the target train in the kth running period, is the posterior running information of the target train in the (k-1) th running period, and A is the preset state transition matrix.

[0057] Taking the current running period as the kth running period as an example, that is, the initial running information of the target train in the current running period, that is, the historical running information of the target train in the historical running period. After obtaining the preset state transition matrix, the initial running information of the target train in the current running period can be calculated according to the above formula 1.

[0058] Since in actual application, the accelerometer itself also has errors, in order to reduce the problem of low train speed measurement accuracy caused by errors, in an embodiment provided in the specification, a state space equation is established according to the mathematical model of uniform acceleration or uniform deceleration to add the change of running acceleration information to the calculation error, but the observation error of the running acceleration information will not be increased in the preset state transition matrix. Specifically, the state space equation established according to the mathematical model of uniform acceleration or uniform deceleration can be referred to formula 2 as follows:

[0059]

[0060] wherein, is the derivative of X, G is the preset state relationship matrix, and W is the process noise of the ATP system in the running process of the target train. Since the ATP system is a periodic running system, after discretization processing of the above formula 2, the following formula 3 can be obtained:

[0061] X k =AX k-1 +GW formula 3

[0062] wherein, is the running speed information and running acceleration information of the kth running period; X k-1 is the running speed information and running acceleration information of the (k-1) th running period. By deducing formula 3, the preset state transition matrix A can be obtained the preset state relationship matrix G process noise w v w is the error calculated for running speed a T is the running period of the ATP system

[0063] After deriving the preset state transition matrix and the preset state relationship matrix according to the above formula 3, the preset state transition matrix and the preset state relationship matrix can be directly used to measure the speed of the train in actual train operation.

[0064] In an embodiment provided in the specification, the state space equation is established according to the mathematical model of uniform acceleration or uniform deceleration, the change of acceleration is added to the calculation error, but the observation error of acceleration is not added to the preset state transition matrix, thereby improving the accuracy and availability of the subsequent train speed measurement process.

[0065] Step 106: Obtain the historical posterior error information of the target train in the historical running period, and calculate the prior error information of the target train in the current running period according to the historical posterior error information.

[0066] After predicting the initial running information of the target train in the current running period, it is necessary to determine the prior error information of the initial running information of the target train in the current running period, so as to correct the initial running information according to the prior error information in the subsequent process, and generate the target running information of the target train in the current running period.

[0067] The historical posterior error information refers to the posterior error information of the target train in the historical running period, which can be specifically the posterior error covariance or the posterior error variance of the target train in the historical running period. The prior error information refers to the prior error information of the target train in the current running period, which can be specifically the prior error covariance or the prior error variance of the target train in the current running period. The forms of the posterior error information and the prior error information can be matrix forms. Specifically, the prior error information of the target train in the current running period can be calculated according to the following formula 4.

[0068] P k - = AP k-1 A T + GQG T Formula 4

[0069] P k - is the prior error information of the target train in the kth running period, which is specifically in the form of a prior error covariance matrix, P k-1 is the posterior error information of the target train in the (k-1)th running period, which is specifically in the form of a posterior error covariance matrix, AT is a transpose matrix of a preset state transition matrix, G T is a transpose matrix of a preset state relationship matrix, Q is a target process error matrix, and specifically, a process noise covariance matrix of process noise W.

[0070] Specifically, after obtaining the historical posterior error information of the target train in the historical running period, the prior error information of the target train in the current running period can be obtained by calculation according to the above formula 4.

[0071] In actual application, the target process error matrix (i.e., the process noise covariance matrix) cannot be obtained by direct observation, and the running scene or running environment of the train during running is different, and the interference received by the ATP system when measuring the speed of the train is also different, so that the speed calculation error and the acceleration calculation error will also change, thereby affecting the speed measurement of the train. Based on this, in one or more embodiments provided in the specification, the prior error information of the target train in the current running period can be calculated according to the different running scenes of the target train, so as to improve the accuracy of calculating the prior error information.

[0072] Based on this, in a specific embodiment provided in the specification, the prior error information of the target train in the current running period is calculated according to the historical posterior error information, including:

[0073] obtaining a preset state transition matrix and a preset state relationship matrix;

[0074] identifying a target running scene of the target train, and obtaining a target process error matrix corresponding to the target running scene;

[0075] calculating the prior error information of the target train in the current running period according to the preset state transition matrix, the preset state relationship matrix, the target process error matrix and the historical posterior error information.

[0076] Wherein, the target running scene refers to the running scene of the target train at present. The running scene can specifically include a standard running scene or an abnormal running scene; the standard running scene refers to the scene of normal running of the target train; the abnormal running scene refers to the running scene of the target train running under interference, for example, the train slipping scene, etc.

[0077] Specifically, after obtaining the historical a posteriori error information of the target train in the historical running cycle, a preset state transition matrix and a preset state relationship matrix are obtained, and a transpose matrix of the preset state transition matrix is determined based on the preset state transition matrix, and a transpose matrix of the preset state relationship matrix is determined based on the preset state relationship matrix. In order to ensure the accuracy of the speed measurement of the target train, the running scene of the target train at present can be identified to accurately obtain the target process error matrix. Specifically, the process error matrix corresponding to the identified target running scene can be determined as the target process error matrix of the target train in the current running cycle. Further, according to the preset state transition matrix, the transpose matrix of the preset state transition matrix, the preset state relationship matrix, the transpose matrix of the preset state relationship matrix, the target process error matrix and the historical a posteriori error information, the prior error information of the target train in the current running cycle is calculated based on the above formula 4.

[0078] Further, the way of obtaining the target process error matrix corresponding to different target running scenes is described.

[0079] In a specific embodiment provided in the present specification, the target process error matrix corresponding to the target running scene is obtained, including:

[0080] In the case where the target running scene is a standard running scene, the standard process error matrix corresponding to the standard running scene is obtained as the target process error matrix.

[0081] In the case where the target running scene is an abnormal running scene, the observation process error matrix corresponding to the abnormal running scene is obtained as the target process error matrix.

[0082] Wherein, the standard process error matrix refers to the process error matrix corresponding to the standard running scene, that is, the process noise covariance matrix corresponding to the standard running scene. The observation process error matrix refers to the process error matrix corresponding to the abnormal running scene, that is, the process noise covariance matrix corresponding to the abnormal running scene. The process error matrix is specifically obtained according to the process noise W, which can be expressed as

[0083] Since the state space equation is established according to the mathematical model of uniform acceleration or uniform deceleration, the error of w v can be ignored, and the error of w a is often caused by the impact rate, and the standard deviation of w a can be taken as 15 cm / s, so that the labeled process error matrix corresponding to the standard running scene is obtained as

[0084] In an abnormal running scenario (for example, a train slipping scenario), the observed process error matrix can be calculated based on the running acceleration information collected by the accelerometer, specifically

[0085] After identifying the target running scenario in which the target train is located and obtaining the target process error matrix corresponding to the target running scenario, the target process error matrix can be substituted into the above formula 4 for calculation to obtain the prior error information of the target train in the current running period.

[0086] Further, since the Kalman filtering algorithm is essentially a recursive algorithm, the calculation of each running period depends on the calculation result of the last running period, therefore, the initial running information and the prior error information P0 - at the start of the train need to be confirmed. Since the train is stationary at the start of power-on, the initial running information and the prior error information P0 - can be set as and

[0087] In an embodiment provided in the present specification, by identifying different running scenarios in which the target train is located, process error matrices corresponding to different running scenarios are determined, and the prior error information of the target train in the current running period is calculated according to the process error matrices corresponding to different running scenarios, thereby improving the accuracy of the calculated prior error information.

[0088] Step 108: Calculate the information fusion weight according to the prior error information.

[0089] After obtaining the prior error information of the target train in the current running period, i.e., the prior error covariance, the weight of the initial running information and the observed running information of the target train in the current running period needs to be determined according to the prior error covariance, so as to fuse the initial running information and the observed running information.

[0090] Wherein, the information fusion weight refers to the weight used for fusing the initial running information and the observed running information. The information fusion weight can be specifically understood as the Kalman gain, and the calculation method thereof is shown in the following formula 5:

[0091] K k =P k - H T (HP k - H T +R) -1 Formula 5

[0092] Wherein, Kk is the information fusion weight of the kth running cycle, H is a preset observation matrix, H T is a transpose matrix of the preset observation matrix, R is a target observation error matrix, and specifically, is an observation noise covariance matrix of observation noise V.

[0093] The preset observation matrix H can be derived by the following formula 6:

[0094] Z k = HX k + V Formula 6

[0095] Wherein, Z k is the observed running information of the target train in the kth running cycle, and can be specifically represented as v mea is the observed running speed information, a mea is the observed running acceleration information, v mea and a mea are collected based on the speed sensor and the accelerometer, and V is the observation noise of the ATP system during the running of the target train. By deriving formula 6, the preset observation matrix H can be obtained, and then the transpose matrix of the preset observation matrix can be determined according to the preset observation matrix.

[0096] Specifically, after obtaining the prior error information of the target train in the current running cycle, the preset observation matrix is obtained, the transpose matrix corresponding to the preset observation matrix is determined based on the preset observation matrix, and the target observation error matrix is obtained. The information fusion weight is calculated according to the above formula 5.

[0097] In actual application, the target observation error matrix (i.e. the observation noise covariance matrix) can often be determined by collecting the observed running speed information of the target train. However, in the process of train running, the running scene of the train is different, and the running speed information of the train in different running scenes is different, which causes the speed calculation error of the speed sensor and the acceleration calculation error of the accelerometer to be different, thereby affecting the train speed measurement. Based on this, in one or more embodiments provided in the specification, the information fusion weight of the target train in the current running cycle can be calculated according to the different running scenes of the target train and the different running speed information in different running scenes, so as to improve the accuracy of calculating the information fusion weight, and further improve the accuracy and availability of obtaining the target running information by fusing the initial running information and the observed running information based on the information fusion weight.

[0098] Based on this, in a specific embodiment provided in the specification, the information fusion weight is calculated according to the prior error information, including:

[0099] obtaining a preset observation matrix;

[0100] identify a target running scenario in which the target train is located, and obtain a target observation error matrix corresponding to the target running scenario;

[0101] According to the preset observation matrix, the target observation error matrix and the prior error information, calculate an information fusion weight.

[0102] Specifically, after obtaining the prior error information of the target train in the current running period, a preset observation matrix is obtained. In order to ensure the accuracy of the speed measurement of the target train, the target observation error matrix can be accurately obtained by identifying the running scenario in which the target train is currently located. Specifically, the observation error matrix corresponding to the identified target running scenario can be determined as the target observation error matrix of the target train in the current running period. Then, according to the preset observation matrix, the transpose matrix of the preset observation matrix, the target observation error matrix and the prior error information, the information fusion weight of the target train in the current running period is calculated based on the above formula 5.

[0103] Further, the way of obtaining the target observation error matrix corresponding to different target running scenarios is described.

[0104] In a specific embodiment provided in the present specification, obtaining the target observation error matrix corresponding to the target running scenario comprises:

[0105] In the case where the target running scenario is a standard running scenario, it is determined whether the observed running speed information of the target train in the current running period reaches a preset speed threshold;

[0106] In the case where the observed running speed information does not reach the preset speed threshold, a fixed observation error matrix corresponding to the standard running scenario is obtained as the target observation error matrix;

[0107] In the case where the observed running speed information reaches the preset speed threshold, a standard running speed error of the target train is calculated according to the observed running speed information, and a first observation error matrix corresponding to the standard running scenario is determined as the target observation error matrix according to the standard running speed error.

[0108] Wherein, the preset speed threshold refers to a low speed threshold of the target train in the running process, that is, a speed limit below which the observed running speed information of the target train is, for example, 3km / h. The standard running speed error refers to the running speed error of the speed sensor when the observed running speed information of the target train reaches the preset speed threshold in the standard running scenario, which can be specifically expressed as v e The standard running speed error can be calculated by the following formula 7:

[0109] v e =v*err Formula 7

[0110] v is the observed running speed information, and err is the calculation error rate of the speed sensor.

[0111] The first observation error matrix refers to the observation error matrix corresponding to the observation running speed information of the target train reaching the preset speed threshold in the standard running scenario, that is, the first observation noise covariance matrix.

[0112] Specifically, in the case that the target running scenario of the target train is a standard running scenario, it is determined whether the observation running speed information of the target train reaches the preset speed threshold. If the observation running speed information of the target train does not reach the preset speed threshold, the fixed observation error matrix corresponding to the standard running scenario is obtained, and the fixed observation error matrix is determined as the target observation error matrix. At this time, the fixed observation error matrix can be represented as If the observation running speed information of the target train reaches the preset speed threshold, the standard running speed error of the target train is calculated according to the observation running speed information of the target train, and the first observation error matrix corresponding to the standard running speed error is further determined according to the standard running speed error. The first observation error matrix is determined as the target observation error matrix. At this time, the first observation error matrix can be represented as

[0113] For example, the preset speed threshold is 3 km / h. If the observation running speed information of the target train is below 3 km / h, the standard running speed error is small at this time, the fixed observation error matrix can be obtained and used as If the observation running speed information of the target train is above 3 km / h, the first observation error matrix can be obtained and used as

[0114] Further, in actual application, the target train may also be in an abnormal running scenario during running, for example, a train slipping scenario. At this time, the corresponding target observation error matrix needs to be determined according to the observation running speed information of the target train in the abnormal running scenario.

[0115] In a specific embodiment provided in the present specification, the target observation error matrix corresponding to the target running scenario is obtained, including:

[0116] In the case that the target running scenario is an abnormal running scenario, the abnormal running speed error of the target train is calculated according to the observation running speed information.

[0117] According to the abnormal running speed error, a second observation error matrix corresponding to the abnormal running scene is determined as a target observation error matrix.

[0118] The second observation error matrix is an observation error matrix corresponding to the abnormal running scene. That is, the observation noise covariance matrix corresponding to the abnormal running scene can be expressed as v slide Specifically, the running speed error of the target train in the abnormal scene is, for example, in the train slip scene, v slide That is, the train slip speed. The calculation method can refer to formula 7, that is, the calculation method of v e .

[0119] Based on this, when the target running scene is an abnormal running scene, the second observation error matrix corresponding to the abnormal running scene is obtained As the target observation error matrix.

[0120] After identifying the target running scene of the target train and obtaining the target observation error matrix corresponding to the target running scene, the target observation error matrix can be substituted into formula 5 for calculation to obtain the information fusion weight of the target train in the current running period.

[0121] In an embodiment provided in the specification, by identifying different running scenes of the target train, the observation error matrices corresponding to different running scenes are determined, and the information fusion weight of the target train in the current running period is calculated according to the observation error matrices corresponding to different running scenes, thereby improving the accuracy of the calculated information fusion weight, and improving the accuracy of the target running information in the subsequent process of fusing the initial running information and the observed running information based on the information fusion weight to generate the target running information.

[0122] Step 110: According to the information fusion weight, the initial running information and the observed running information are fused to obtain the target running information of the target train in the current running period.

[0123] After obtaining the information fusion weight, the initial running information and the observed running information of the target train in the current running period can be information fused according to the information fusion weight to obtain the target running information of the target train in the current running period. The information fusion process can refer to formula 8 as follows:

[0124]

[0125] Wherein, Target running information of the target train in the kth running cycle. After obtaining the initial running information of the target train in the current running cycle, the information fusion weight, the observed running information, and the preset observation matrix, the target running information of the target train in the current running cycle can be calculated according to the above formula 8.

[0126] Since the prior error information corresponding to each running cycle is necessarily used in the process of calculating the information fusion weight corresponding to each running cycle in actual application, after the target running information is generated, the prior error information of the current running cycle needs to be updated to obtain the current posterior error information of the current running cycle, so that the current posterior error information is used for the calculation of the prior error information of the next running cycle.

[0127] Therefore, in a specific embodiment provided in the specification, after the information fusion weight is calculated according to the prior error information, the method further comprises:

[0128] obtaining a preset observation matrix;

[0129] updating the prior error information according to the preset observation matrix and the information fusion weight to obtain the current posterior error information of the target train in the current cycle.

[0130] The current posterior error information refers to the posterior error information of the target train in the current running cycle, and can be specifically the posterior error covariance or the posterior error variance of the target train in the current running cycle.

[0131] P k =(1-K k H)P k - Formula 9

[0132] P k is the posterior error information of the target train in the kth running cycle, and is specifically a posterior error covariance matrix. Specifically, a preset observation matrix is obtained, and the current posterior error information of the target train in the current running cycle is obtained by calculating based on formula 9 according to the information fusion weight and the prior error information that have been calculated.

[0133] Further, since two speed sensors, i.e., a first speed sensor and a second speed sensor, are deployed in the train, after the target running information is fused, the first target running speed information and the second target running speed information in the target running information need to be fused to obtain fused target fusion speed information.

[0134] In a specific embodiment provided in the specification, the target running information comprises first target running speed information and second target running speed information;

[0135] After obtaining the target train target running information in the current running cycle, the method further comprises:

[0136] Obtaining a first calculation factor and a second calculation factor from the current posterior error information;

[0137] According to the first calculation factor and the second calculation factor, the first target running speed information and the second target running speed information are fused to obtain target fusion speed information.

[0138] Wherein, the first calculation factor is the calculation factor corresponding to the first target running speed information, which is also the calculation standard deviation of the first target running speed information; the second calculation factor is the calculation factor corresponding to the second target running speed information, which is also the calculation standard deviation of the second target running speed information. The first calculation factor can be obtained from the current posterior error information corresponding to the first target running speed information (i.e. the first current posterior error covariance matrix), and the second calculation factor can be obtained from the current posterior error information corresponding to the second target running speed information (i.e. the second current posterior error covariance matrix). The specific implementation of fusing the first target running speed information and the second target running speed information to obtain the target fusion speed information can be referred to formula 10 as follows:

[0139]

[0140] The target fusion speed information is v1, the first target running speed information is v2, the second target running speed information is δ1, and the second calculation factor is δ2.

[0141] In the prior error information P k - is updated, and the current posterior error information P k is obtained. From the current posterior error information P k , the calculation standard deviation of the first target running speed information v1 and the calculation standard deviation of the second target running speed information v2 are obtained, which are respectively determined as the first calculation factor δ1 and the second calculation factor δ2. According to the above formula 10, the first target running speed information v1 and the second target running speed information v2 are fused to obtain the target fusion speed information

[0142] In one embodiment provided in the specification, after obtaining the first target running speed information and the second target running speed information based on the Kalman filtering algorithm, the fusion between the first target running speed information and the second target running speed information is performed through variance calculation to obtain the target fusion speed information, thereby improving the accuracy of train speed measurement.

[0143] The train speed measurement method provided in the specification comprises: obtaining historical running information of a target train in a historical running period and observed running information of the target train in a current running period, wherein the running information comprises running speed information and running acceleration information; generating initial running information of the target train in the current running period according to the historical running information; obtaining historical posterior error information of the target train in the historical running period, and calculating prior error information of the target train in the current running period according to the historical posterior error information; calculating an information fusion weight according to the prior error information; and fusing the initial running information and the observed running information according to the information fusion weight to obtain target running information of the target train in the current running period.

[0144] An embodiment of the specification realizes measuring the speed of a target train according to running speed information and running acceleration information of the target train. In the process of measuring the speed of the target train, not only the error caused by a speed sensor in the target train is considered, but also the error caused by an accelerometer is considered. Further, different target process error matrices and target observation error matrices corresponding to different target running scenarios of the target train are obtained by comprehensively considering the different target running scenarios, different calculation methods are implemented for different target running scenarios, the problem of low accuracy of measuring the speed of the train caused by errors is reduced, and the accuracy and usability of train speed measurement are improved.

[0145] The following describes the train speed measurement method in combination with the accompanying Figure 3 The train speed measurement method provided in the specification is further described by taking the application of the train speed measurement method in a rail transit scenario as an example. Wherein, Figure 3 A processing process flowchart of a train speed measurement method provided by an embodiment of the specification is shown, which specifically comprises the following steps:

[0146] Step 302: Obtain historical running information of a target train in a k-1th running period and a preset state transition matrix A.

[0147] Step 304: Generate initial running information of the target train in a kth running period according to the historical running information and the preset state transition matrix A.

[0148] Specifically, the initial running information is calculated and generated according to the formula

[0149] Step 306: Obtain posterior error covariance P of the target train in the k-1th running period k-1 ​and a preset state relationship matrix G, and obtaining a target process noise covariance matrix Q according to a target running scenario in which the target train is located.

[0150] Step 308: calculating a prior error covariance P k-1 k - .

[0151] Specifically, the prior error covariance P k - is calculated according to a formula P k-1 A T + GQG T . k - .

[0152] Step 310: obtaining a preset observation matrix H, and obtaining a target observation noise covariance matrix R according to a target running scenario in which the target train is located.

[0153] Step 312: calculating an information fusion weight K k - of the target train in the kth running period according to the prior error covariance P k .

[0154] Specifically, the information fusion weight K k is calculated according to a formula K k - H T (HP k - H T + R) -1 .

[0155] Step 314: obtaining observed running information z k of the target train in the kth running period.

[0156] Step 316: calculating target running information of the target train in the kth running period according to the initial running information k , the information fusion weight K k , the observed running information z k and the preset observation matrix H.

[0157] Specifically, the target running information is calculated according to a formula

[0158] ​Step 318: obtaining the posterior error covariance P based on the information fusion weight K k , the preset observation matrix H and the prior error covariance P k - , and updating to obtain the posterior error covariance P of the kth running period k .

[0159] Specifically, according to the formula P k = (1-K k H) P k - update the posterior error covariance P k .

[0160] Step 320: obtaining a first calculation factor δ1 of the first target running speed information v1 and a second calculation factor δ2 of the second target running speed information v2 from the posterior error covariance P k .

[0161] Step 322: according to the first target running speed information v1, the second target running speed information v2, the first calculation factor δ1 and the second calculation factor δ2, fusing and calculating to obtain target fusion speed information v of the target train

[0162] Specifically, according to the formula fusion to obtain the target fusion speed information v

[0163] An embodiment of the present specification realizes that the data of the speed sensor and the accelerometer are processed through the Kalman filtering algorithm, not only considering the error caused by the speed sensor in the target train, but also considering the error caused by the accelerometer, reducing the problem that the accuracy of measuring the speed of the train is low due to the error, and improving the accuracy and availability of the train speed measurement.

[0164] Corresponding to the above method embodiment, the present specification also provides a train speed measurement device embodiment, Figure 4 a structure schematic diagram of a train speed measurement device provided by an embodiment of the present specification is shown. As Figure 4 shown, the device comprises:

[0165] The acquisition module 402 is configured to acquire historical running information of a target train in a historical running period and observation running information of the target train in a current running period, wherein the running information comprises running speed information and running acceleration information;

[0166] The generation module 404 is configured to generate initial running information of the target train in the current running period according to the historical running information;

[0167] The first calculation module 406 is configured to acquire historical posterior error information of the target train in the historical running period, and calculate prior error information of the target train in the current running period according to the historical posterior error information;

[0168] The second calculation module 408 is configured to calculate an information fusion weight according to the prior error information;

[0169] The fusion module 410 is configured to fuse the initial running information and the observed running information according to the information fusion weight, to obtain target running information of the target train in the current running period.

[0170] Optionally, the generation module 404 is further configured to:

[0171] acquire a preset state transition matrix;

[0172] generate the initial running information of the target train in the current running period according to the preset state transition matrix and the historical running information.

[0173] Optionally, the first calculation module 406 is further configured to:

[0174] acquire a preset state transition matrix and a preset state relationship matrix;

[0175] identify a target running scene in which the target train is located, and acquire a target process error matrix corresponding to the target running scene;

[0176] calculate the prior error information of the target train in the current running period according to the preset state transition matrix, the preset state relationship matrix, the target process error matrix and the historical posterior error information.

[0177] Optionally, the first calculation module 406 is further configured to:

[0178] in a case where the target running scene is a standard running scene, acquire a standard process error matrix corresponding to the standard running scene as the target process error matrix;

[0179] in a case where the target running scene is an abnormal running scene, acquire an observed process error matrix corresponding to the abnormal running scene as the target process error matrix.

[0180] Optionally, the second calculation module 408 is further configured to:

[0181] acquire a preset observation matrix;

[0182] identify a target operation scenario in which the target train is located, and obtain a target observation error matrix corresponding to the target operation scenario;

[0183] According to the preset observation matrix, the target observation error matrix and the prior error information, calculate an information fusion weight.

[0184] Optionally, the second calculation module 408 is further configured to:

[0185] In the case where the target operation scenario is a standard operation scenario, determine whether the observation operation speed information of the target train in the current operation period reaches a preset speed threshold;

[0186] In the case where the observation operation speed information does not reach the preset speed threshold, obtain a fixed observation error matrix corresponding to the standard operation scenario as a target observation error matrix;

[0187] In the case where the observation operation speed information reaches the preset speed threshold, calculate a standard operation speed error of the target train according to the observation operation speed information, and determine a first observation error matrix corresponding to the standard operation scenario as a target observation error matrix according to the standard operation speed error.

[0188] Optionally, the second calculation module 408 is further configured to:

[0189] In the case where the target operation scenario is an abnormal operation scenario, calculate an abnormal operation speed error of the target train according to the observation operation speed information;

[0190] According to the abnormal operation speed error, determine a second observation error matrix corresponding to the abnormal operation scenario as a target observation error matrix.

[0191] Optionally, the device further comprises an updating module configured to:

[0192] Obtain a preset observation matrix;

[0193] According to the preset observation matrix and the information fusion weight, update the prior error information to obtain current posterior error information of the target train in the current period.

[0194] Optionally, the target operation information includes first target operation speed information and second target operation speed information;

[0195] The device further comprises a speed fusion module configured to:

[0196] Obtain a first calculation factor and a second calculation factor from the current posterior error information;

[0197] According to the first calculation factor and the second calculation factor, the first target running speed information and the second target running speed information are fused to obtain target fused speed information.

[0198] An embodiment of the present specification realizes that, according to the running speed information and the running acceleration information of the target train, the target train is measured, in the process of measuring the target train, not only the error caused by the speed sensor in the target train is considered, but also the error caused by the accelerometer is considered. Further, different target running scenarios of the target train are further considered to obtain target process error matrices and target observation error matrices corresponding to different running scenarios, different calculation methods are implemented for different target running scenarios, the problem of low accuracy of measuring the train caused by the error is reduced, and the accuracy and availability of train speed measurement are improved.

[0199] The above is a schematic scheme of a train speed measurement device according to an embodiment of the present specification. It should be noted that the technical scheme of the train speed measurement device belongs to the same concept as the technical scheme of the train speed measurement method described above, and the details of the technical scheme of the train speed measurement device that are not described in detail can be referred to the description of the technical scheme of the train speed measurement method.

[0200] Figure 5 A structural block diagram of a computing device 500 according to an embodiment of the present specification is shown. The components of the computing device 500 include but are not limited to a memory 510 and a processor 520. The processor 520 is connected to the memory 510 through a bus 530, and a database 550 is used to save data.

[0201] The computing device 500 also includes an access device 540 that enables the computing device 500 to communicate via one or more networks 560. Examples of such networks include a public switched telephone network (PSTN), a local area network (LAN), a wide area network (WAN), a personal area network (PAN), or combinations of such networks, such as the Internet. The access device 540 can include one or more of any type of network interface (for example, a network interface card (NIC)), such as an IEEE 802.11 wireless local area network (WLAN) wireless interface, a Worldwide Interoperability for Microwave Access (Wi-MAX) interface, an Ethernet interface, a Universal Serial Bus (USB) interface, a cellular network interface, a Bluetooth interface, a Near Field Communication (NFC) interface, or the like.

[0202] In one embodiment of the present specification, the above-mentioned components of the computing device 500 and other components not shown in the Figure 5 may be connected to each other, such as through a bus. It should be understood that Figure 5 The computing device structure diagram shown is for the purpose of example only, and is not a limitation on the scope of the present specification. Other components can be added or replaced as needed by those skilled in the art.

[0203] The computing device 500 can be any type of stationary or mobile computing device, including a mobile computer or mobile computing device (for example, a tablet computer, a personal digital assistant, a laptop computer, a notebook computer, a netbook, and the like), a mobile phone (for example, a smartphone), a wearable computing device (for example, a smartwatch, smart glasses, and the like), or other types of mobile devices, or a stationary computing device such as a desktop computer or a personal computer (PC). The computing device 500 can also be a mobile or stationary server.

[0204] wherein the processor 520 implements the steps of the train speed measurement method when executing the computer program / instructions.

[0205] The above is a schematic scheme of the computing device of the embodiment. It should be noted that the technical scheme of the computing device and the technical scheme of the train speed measurement method described above belong to the same concept, and the details of the technical scheme of the computing device that are not described in detail can be referred to the description of the technical scheme of the train speed measurement method.

[0206] An embodiment of the present specification also provides a computer readable storage medium storing computer programs / instructions, which, when executed by a processor, implement the steps of the train speed measurement method described above.

[0207] The above is a schematic scheme of the computer readable storage medium of the embodiment. It should be noted that the technical scheme of the storage medium and the technical scheme of the train speed measurement method described above belong to the same concept, and the details of the technical scheme of the storage medium that are not described in detail can be referred to the description of the technical scheme of the train speed measurement method.

[0208] An embodiment of the present specification also provides a computer program product comprising computer programs / instructions, which, when executed by a processor, implement the steps of the train speed measurement method described above.

[0209] The above is a schematic scheme of the computer program product of the embodiment. It should be noted that the technical scheme of the computer program product and the technical scheme of the train speed measurement method described above belong to the same concept, and the details of the technical scheme of the computer program product that are not described in detail can be referred to the description of the technical scheme of the train speed measurement method.

[0210] The above describes specific embodiments of the present specification. Other embodiments are within the scope of the appended claims. In some cases, the acts or steps recited in the claims can be performed in a different order than those in the embodiments and still achieve desirable results. In addition, the processes depicted in the figures do not necessarily require the particular order shown or sequential order to achieve the desired results. In certain implementations, multitasking and parallel processing can be advantageous.

[0211] The computer programs / instructions include computer program codes, which can be in the form of source codes, object codes, executable files, or some intermediate forms, etc. The computer readable medium can include any entity or device capable of carrying the computer program codes, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc.

[0212] It should be noted that, for the aforementioned method embodiments, the sequences of the actions are described for ease of description. However, it is to be understood that the sequences of the actions can be changed according to the embodiments of the present application, and more actions can be added, or existing actions can be removed, depending on the actual conditions. Moreover, the embodiments described in the specification are preferred embodiments only and do not limit the scope of the application.

[0213] In the above embodiments, the description of each embodiment is focused on the description of the embodiment. The parts not described in detail in a certain embodiment can be referred to the relevant description of other embodiments.

[0214] The preferred embodiments of the present application disclosed above are only used to clarify the present application. The alternative embodiments do not describe all the details and limit the application to the specific embodiments described. Obviously, many modifications and changes can be made according to the content of the present application. The embodiments are selected and described in detail in order to better explain the principles and practical applications of the present application, so that those skilled in the art can well understand and use the present application. The present application is limited by the claims and their full scope and equivalents.

Claims

1. A method of train speed measurement, characterized by, The method comprises the following steps: obtaining historical running information of a target train in a historical running period and observed running information of the target train in a current running period, wherein the running information comprises running speed information and running acceleration information, the historical running period is a previous running period of the current running period, and the historical running information is posterior running information of the target train in the historical running period; generating initial running information of the target train in the current running period according to the historical running information; obtaining historical posterior error information of the target train in the historical running period, and calculating prior error information of the target train in the current running period according to the historical posterior error information; calculating an information fusion weight according to the prior error information, wherein the calculation of the information fusion weight according to the prior error information comprises obtaining a preset observation matrix, identifying a target running scenario in which the target train is located, obtaining a target observation error matrix corresponding to the target running scenario, and calculating the information fusion weight according to the preset observation matrix, the target observation error matrix and the prior error information, wherein the obtaining of the target observation error matrix corresponding to the target running scenario comprises, in the case that the target running scenario is a standard running scenario, judging whether observed running speed information of the target train in the current running period reaches a preset speed threshold, in the case that the observed running speed information does not reach the preset speed threshold, obtaining a fixed observation error matrix corresponding to the standard running scenario as the target observation error matrix, in the case that the observed running speed information reaches the preset speed threshold, calculating a standard running speed error of the target train according to the observed running speed information, and determining a first observation error matrix corresponding to the standard running scenario as the target observation error matrix according to the standard running speed error, and in the case that the target running scenario is an abnormal running scenario, calculating an abnormal running speed error of the target train according to the observed running speed information, and determining a second observation error matrix corresponding to the abnormal running scenario as the target observation error matrix according to the abnormal running speed error; fusing the initial running information and the observed running information according to the information fusion weight to obtain target running information of the target train in the current running period.

2. The method of claim 1, wherein, The method comprises the following steps: obtaining a preset state transition matrix; generating initial running information of the target train in the current running period according to the preset state transition matrix and the historical running information.

3. The method of claim 1, wherein, The method comprises the following steps: obtaining a preset state transition matrix and a preset state relationship matrix; identifying a target running scenario in which the target train is located, and obtaining a target process error matrix corresponding to the target running scenario. According to the preset state transition matrix, the preset state relationship matrix, the target process error matrix and the historical posterior error information, prior error information of the target train in the current operation cycle is calculated.

4. The method of claim 3, wherein, The target process error matrix corresponding to the target operation scene is obtained, including: In the case that the target operation scene is a standard operation scene, a standard process error matrix corresponding to the standard operation scene is obtained as the target process error matrix; In the case that the target operation scene is an abnormal operation scene, an observation process error matrix corresponding to the abnormal operation scene is obtained as the target process error matrix.

5. The method of claim 1, wherein, After the information fusion weight is calculated according to the prior error information, the method further includes: A preset observation matrix is obtained; According to the preset observation matrix and the information fusion weight, the prior error information is updated to obtain current posterior error information of the target train in the current operation cycle.

6. The method of claim 5, wherein, The target operation information includes first target operation speed information and second target operation speed information; After the target operation information of the target train in the current operation cycle is obtained, the method further includes: A first calculation factor and a second calculation factor are obtained from the current posterior error information; According to the first calculation factor and the second calculation factor, the first target operation speed information and the second target operation speed information are fused to obtain target fusion speed information.

7. A train speed measuring device, characterised in that, including: The obtaining module is configured to obtain historical operation information of a target train in a historical operation cycle and observed operation information of the target train in a current operation cycle, wherein the operation information includes operation speed information and operation acceleration information, the historical operation cycle is a last operation cycle of the current operation cycle, and the historical operation information is posterior operation information of the target train in the historical operation cycle; The generating module is configured to generate initial operation information of the target train in the current operation cycle according to the historical operation information; The first calculation module is configured to obtain historical posterior error information of the target train in the historical operation cycle and calculate prior error information of the target train in the current operation cycle according to the historical posterior error information; The second calculation module is configured to calculate an information fusion weight according to the prior error information. The calculation of the information fusion weight according to the prior error information includes obtaining a preset observation matrix, identifying a target running scenario in which the target train is located, obtaining a target observation error matrix corresponding to the target running scenario, and calculating the information fusion weight according to the preset observation matrix, the target observation error matrix, and the prior error information. The obtaining of the target observation error matrix corresponding to the target running scenario includes, in a case where the target running scenario is a standard running scenario, judging whether observation running speed information of the target train in the current running period reaches a preset speed threshold, in a case where the observation running speed information does not reach the preset speed threshold, obtaining a fixed observation error matrix corresponding to the standard running scenario as the target observation error matrix, in a case where the observation running speed information reaches the preset speed threshold, calculating a standard running speed error of the target train according to the observation running speed information, and determining a first observation error matrix corresponding to the standard running scenario as the target observation error matrix according to the standard running speed error, and in a case where the target running scenario is an abnormal running scenario, calculating an abnormal running speed error of the target train according to the observation running speed information, and determining a second observation error matrix corresponding to the abnormal running scenario as the target observation error matrix according to the abnormal running speed error. The fusion module is configured to fuse the initial running information and the observation running information according to the information fusion weight to obtain target running information of the target train in the current running period.

8. A computing device, comprising: a memory and a processor; the memory is configured to store computer programs / instructions, and the processor is configured to execute the computer programs / instructions, wherein the computer programs / instructions are configured to implement the steps of the method in any one of claims 1-6 when executed by the processor.

9. A computer readable storage medium storing computer programs / instructions, characterized in that, The computer programs / instructions are configured to implement the steps of the method in any one of claims 1-6 when executed by the processor.

10. A computer program product comprising computer programs / instructions, characterized in that, The computer programs / instructions are configured to implement the steps of the method in any one of claims 1-6 when executed by the processor. The computer programs / instructions are configured to implement the steps of the method in any one of claims 1-6 when executed by the processor.

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