CN113038519B - Intelligent monitoring system for rail transit train-ground wireless communication and decision tree method - Google Patents
Intelligent monitoring system for rail transit train-ground wireless communication and decision tree method Download PDFInfo
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Abstract
The invention discloses a rail transit vehicle-ground wireless communication intelligent monitoring system and a decision tree method, which comprise a fixed monitoring small station, a vehicle-mounted monitoring small station and monitoring signal intelligent analysis software, wherein the fixed monitoring small station is arranged near a base station and is used for monitoring LTE-M uplink/downlink frequency bands and adjacent or adjacent sector signal carrier-to-interference ratios in real time, carrying out uninterrupted frequency sweep test on the whole subway line wireless network, and caching the level, the carrier-to-noise ratio and the channel bandwidth of a received wireless signal; the vehicle-mounted monitoring small station is arranged on a vehicle and used for being connected with a train position information interface, binding a wireless measurement index and measurement position information of a measurement point by combining a position calibration module and transmitting the bound wireless measurement index and the measurement position information to monitoring signal intelligent analysis software; and the intelligent monitoring signal analysis software is used for realizing the online monitoring of the coverage quality of the subway train-ground wireless signals by utilizing the time-frequency characteristic big data analysis of the data packet, and identifying the interference pattern and the source positioning of the interference signal by measuring the signal intensity of the interference signal and combining an ID3 decision tree algorithm.
Description
Technical Field
The invention relates to the technical field of rail transit communication, in particular to a rail transit vehicle-ground wireless communication intelligent monitoring system and a decision tree method.
Background
In recent years, with the rapid development of rail transit, a subway CBTC signal system adopts a data communication system based on a 2.4GHz frequency band and a 1.8G frequency band of LTE-M to carry data exchange work between vehicles and ground, and a wireless signal is abnormal or interfered in CBTC vehicle-ground communication, which may cause an operation failure. Because of a large number of interference signals in a wireless environment and a complex environment, a higher requirement is provided for monitoring the wireless environment of a subway wireless communication system, and the traditional operation and maintenance mode cannot meet increasingly complex operation and maintenance management requirements.
The system is provided with the train-ground communication wireless monitoring equipment on the train and the appointed station, the condition monitoring of the wireless network environment of the operation line is efficiently and quickly completed, the train-ground communication signal coverage quality of different sections of different lines is output, and potential wireless interference can be found.
Disclosure of Invention
In order to overcome the defects in the prior art, the invention provides an intelligent monitoring system for rail transit train-ground wireless communication and a decision tree method.
In order to achieve the above purpose, the technical solution for solving the technical problem is as follows:
the invention discloses an intelligent monitoring system for rail transit train-ground wireless communication, which comprises a fixed monitoring small station, a vehicle-mounted monitoring small station and intelligent monitoring signal analysis software, wherein the fixed monitoring small station comprises:
the fixed monitoring small station is arranged near a base station and is used for monitoring LTE-M uplink/downlink frequency bands, adjacent or adjacent sector signal carrier-to-interference ratio C/I in real time, carrying out uninterrupted frequency sweep test on the whole subway line wireless network, caching received wireless signal level, carrier-to-noise ratio and channel bandwidth, and transmitting the wireless signal level, carrier-to-interference ratio and channel bandwidth to a subway intranet through a remote data transmission module;
the vehicle-mounted monitoring small station is arranged on a vehicle and used for connecting with a train position information interface, binding a wireless measurement index of a measurement point with measurement position information by combining a position calibration module, and transmitting data to the monitoring signal intelligent analysis software through an IP data network;
the monitoring signal intelligent analysis software is used for developing a wireless system intelligent analysis algorithm through a wireless air interface, a convergence network port and a log port, and further utilizing the time-frequency characteristic big data analysis of a data packet, so that the online monitoring of the CBTC wireless signal coverage quality of the subway station is realized, and a typical interference pattern and the source positioning of an interference signal are identified by measuring the signal strength RSSI of the interference signal and combining an ID3 decision tree algorithm.
Furthermore, the system also comprises a tunnel internal positioning module, wherein the tunnel internal positioning module is installed in a subway tunnel and used for assisting the vehicle-mounted monitoring substation in determining the current train position information.
Furthermore, the system also comprises an electromagnetic environment management module, wherein the electromagnetic environment management module is arranged along the subway and is used for monitoring the coverage and the interference condition of electromagnetic signals along the subway.
Preferably, the frequency range of the sweep mode of the fixed monitoring small station is a continuous rail transit wireless communication frequency range.
Preferably, the monitoring mode frequency range of the fixed monitoring small station is a discontinuous available frequency range of the monitored system.
Furthermore, all test data recorded in the vehicle-mounted monitoring small station comprise measuring time and measuring positions, can be derived in a universal format, and can be remotely accessed into the monitoring signal intelligent analysis software.
Furthermore, the vehicle-mounted monitoring small station has the functions of static and dynamic data management of a subway wireless system and can perform wireless interference analysis.
Further, the vehicle-mounted monitoring small station is provided with a high-pointing interference source positioning antenna.
The invention also discloses a decision tree method in the rail transit vehicle-ground wireless communication intelligent monitoring system, which utilizes the rail transit vehicle-ground wireless communication intelligent monitoring system to monitor and comprises the following steps:
step 1: carrying out data initialization on the interference signal sample and the preprocessed characteristic parameter sample;
and 2, step: randomly selecting a part of samples as a training set and the other part of samples as a testing set from the acquired interference signal samples;
and 3, step 3: using training set to create decision tree classifier, calculating information gain of base station identification code, up/down voice quality, voice transmission time, signal carrier-to-interference ratio C/I, whether there is third order intermodulation relation and bottom noise rise value, the information gain expression is:
wherein, P i Is the probability of the ith class, S is the set of samples,
the probability that the number of jth class of feature A accounts for all classes of feature A, P ij The probability of occupying different classes of the sample set S in the jth class under the characteristic A is obtained;
and 4, step 4: arranging the information gains of the characteristic parameters according to the magnitude sequence as the sequence for identifying the interference signals;
and 5: fitting the established decision tree by using a test set to obtain an identification rate graph of the interference pattern;
step 6: establishing a loss function of the whole minimized decision tree through cross validation of error values of a training set and a test set, and finding out the maximum pruning level of the minimum error;
and 7: and pruning the established decision tree according to the maximum pruning level to prevent the decision tree from being over-fitted.
Due to the adoption of the technical scheme, compared with the prior art, the invention has the following advantages and positive effects:
according to the intelligent monitoring system for wireless communication of the rail transit train and the ground, provided by the invention, the whole subway line wireless network is subjected to frequency sweep test in 24 hours, and the specified frequency band is subjected to uninterrupted monitoring, so that the safety and reliability of the subway train wireless network environment are ensured. When the monitored signal is abnormal, an interference signal sample is automatically generated, a typical interference pattern can be simply and quickly identified and the source of the interference signal can be positioned by combining an ID3 decision tree algorithm, and the interference pattern is automatically reported to a data processing center. The system has strong real-time performance, has little influence on a rail transit operation system, reduces the maintenance workload, reduces the construction and operation maintenance cost of a subway signal system, and particularly avoids the workload of manual gradual investigation in the field of urban rail transit.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below. It is obvious that the drawings in the following description are only some embodiments of the invention, and that for a person skilled in the art, other drawings can also be derived from them without inventive effort. In the drawings:
FIG. 1 is a schematic block diagram of an intelligent monitoring system for train-ground wireless communication according to the present invention;
FIG. 2 is a flow chart of the pattern recognition of the interference pattern of the ID3 decision tree algorithm of the present invention;
FIG. 3 is a schematic diagram of a decision tree generated by the present invention.
Detailed Description
While the embodiments of the present invention will be described and illustrated in detail with reference to the accompanying drawings, it is to be understood that the invention is not limited to the specific embodiments disclosed, but is intended to cover various modifications, equivalents, and alternatives falling within the scope of the invention as defined by the appended claims.
Example one
Referring to fig. 1, the invention discloses an intelligent monitoring system for rail transit train-ground wireless communication, which comprises a fixed monitoring small station, a vehicle-mounted monitoring small station and intelligent monitoring signal analysis software, wherein:
the fixed monitoring small station is arranged near a base station and is used for monitoring the LTE-M up-down/row frequency band, the carrier-to-interference ratio C/I of adjacent or neighboring sector signals in real time, carrying out uninterrupted frequency sweep test on the whole subway line wireless network, caching the received wireless signal level, the carrier-to-noise ratio and the channel bandwidth, and transmitting the wireless signal level, the carrier-to-interference ratio and the channel bandwidth to a subway intranet through a remote data transmission module. Specifically, the fixed monitoring small station comprises an antenna installed on the same tower as the LTE base station and a full-frequency-band frequency sweeping monitoring terminal used for a wireless frequency sweeping working mode and a wireless monitoring working mode, the frequency-band frequency sweeping monitoring terminal adopts a wireless sensor to build a radio frequency module, multi-frequency-band wireless signal acquisition and detection are achieved, and frequency-band scanning and field intensity detection can be completed. And a baseband module is constructed by using the FPGA chip, so that the analysis of the CBTC wireless signal and the external interference signal is realized, the information such as the ID, the MAC address and the like of the wireless signal is obtained, and the identification and the positioning of the wireless signal are realized. One fixed monitoring substation monitors 2 base stations erected adjacent to the fixed monitoring substation simultaneously, the existing power grid of the subway is utilized for supplying power, and the fixed monitoring substation is accessed into a completely isolated subway communication network through a remote data transmission module.
In this embodiment, the fixed monitoring small station has two working modes, one is a wireless frequency sweep working mode, and in the working mode, a frequency sweep test is performed on the whole subway line wireless network, and the level, the carrier-to-noise ratio, and the channel bandwidth of a received wireless signal are cached for 24 hours. The frequency range of the sweep mode of the fixed monitoring small station is a continuous rail transit wireless communication frequency range, such as 30 MHz-3 GHz. The other is a wireless monitoring mode, and the frequency range of the monitoring mode of the fixed monitoring small station is a discontinuous available frequency range of the monitored system. Under the working mode, signals of frequency bands of 900MHz, 1.8GHz and 2.4GHz can be monitored uninterruptedly, and the level, the carrier-to-noise ratio and the channel bandwidth of the received wireless signals are analyzed, and system characteristic parameters, data messages, the error rate and the transmission bandwidth are stored and playback is supported.
The vehicle-mounted monitoring small station is mounted on a vehicle, one end of the vehicle-mounted monitoring small station is connected with an antenna outside a train compartment through a feeder line, the other end of the vehicle-mounted monitoring small station is connected with a train position information interface, a position calibration module is combined to bind wireless measurement indexes and measurement position information of a measurement point, and data are transmitted to the monitoring signal intelligent analysis software through an IP data network. In this embodiment, the vehicle-mounted monitoring small station not only has a frequency sweeping function, but also has a vehicle-mounted positioning function. The vehicle-mounted monitoring small station stores sweep frequency data acquired at a certain time point, and meanwhile, the running position information of the train at the current moment is acquired by combining the position calibration module and the information of the train at the current moment, and the information of the train and the sweep frequency data is bound, so that the signal interference analysis of the train in different states is simplified.
The monitoring signal intelligent analysis software is used for developing a wireless system intelligent analysis algorithm through a wireless air interface, a convergence network port and a log port, and further utilizing the big data analysis of the time-frequency characteristics of a data packet, so that the online monitoring of the CBTC wireless signal coverage quality of the subway station is realized, and typical interference patterns and interference signal source positioning can be simply and quickly identified by measuring the signal strength RSSI of interference signals and combining an ID3 decision tree algorithm. And carrying out statistical analysis and abnormal early warning on professional control monitoring data such as rail transit vehicles, signals and power supply by using a big data algorithm. In this embodiment, the intelligent monitoring signal analysis software analyzes and summarizes the frequency sweeping information collected from the data processing center, and establishes an LTE-M signal database according to the LTE-M signal monitoring result and in combination with the LTE-M base station signal identification function. And when the monitoring signal is not in the normal range, recording the abnormal signal, giving an alarm, performing multiple tests to form a template, recording the maximum value, 95%, 70%, 50% and 10% values, and counting the variance.
Furthermore, the intelligent monitoring system further comprises a tunnel internal positioning module, wherein the tunnel internal positioning module is installed in a subway tunnel and used for assisting the vehicle-mounted monitoring small station in determining the current train position information.
Furthermore, the intelligent monitoring system further comprises an electromagnetic environment management module, wherein the electromagnetic environment management module is installed along the subway and used for monitoring the coverage and interference conditions of electromagnetic signals along the subway.
Preferably, the LTE-M minimum received signal level is-95 dBm.
Furthermore, all test data recorded in the vehicle-mounted monitoring small station comprise measuring time and measuring positions, can be derived in a universal format, and can be remotely accessed into the monitoring signal intelligent analysis software.
Furthermore, the vehicle-mounted monitoring small station has the functions of static and dynamic data management of a subway wireless system and can perform wireless interference analysis.
Further, the vehicle-mounted monitoring small station is provided with a high-pointing interference source positioning antenna.
Example two
The invention also discloses a decision tree method in a rail transit vehicle-ground wireless communication intelligent monitoring system, which is a pattern recognition flow chart of an ID3 decision tree algorithm interference pattern shown in figure 2, and comprises the steps of collecting and processing interference signals, extracting characteristic values of the interference signals, selecting test samples and training samples, carrying out decision tree algorithm classification recognition on training sample data, finally carrying out cross validation on error values of the training set and the testing set to find the maximum pruning level with the minimum error, and pruning the trained decision tree, thereby improving the correct recognition rate of the decision tree, and specifically comprises the following steps:
step 1: carrying out data initialization on the interference signal sample and the preprocessed characteristic parameter sample;
step 2: randomly selecting a part of samples from the collected interference signal samples as a training set, and using the other part of samples as a test set;
and step 3: a decision tree classifier is created by utilizing a training set, and information gains of base station identification codes, uplink and downlink voice quality, voice transmission time, signal carrier-to-interference ratio C/I, the existence of a third-order intermodulation relation and a bottom noise rise value are calculated, wherein an information gain expression is shown in a formula (1):
wherein, P i Is the probability of the ith class, S is the set of samples,
the probability that the number of jth class of feature A accounts for all classes of feature A, P ij The probability of occupying different classes of the sample set S in the jth class under the characteristic A is obtained;
and 4, step 4: arranging the information gains of the characteristic parameters according to the magnitude sequence as the sequence for identifying the interference signals;
and 5: fitting the established decision tree by using a test set to obtain an identification rate graph of the interference pattern;
step 6: establishing a loss function of the whole minimized decision tree through cross validation of error values of a training set and a test set, and finding out the maximum pruning level of the minimum error;
and 7: and pruning the established decision tree according to the maximum pruning level to prevent the decision tree from being over-fitted.
Referring to fig. 3, the preprocessed monitoring data are compared with the characteristic values of the base station identification code, the uplink/downlink voice quality, the voice transmission time, the signal carrier-to-interference ratio C/I, the presence or absence of the third-order intermodulation relation, the bottom noise rise value and the like in sequence, so as to gradually complete the pattern classification of the monitoring data.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (9)
1. The utility model provides a rail transit vehicle ground wireless communication intelligent monitoring system which characterized in that, includes fixed monitoring little station, on-vehicle monitoring little station and monitoring signal intelligent analysis software, wherein:
the fixed monitoring small station is arranged near a base station and is used for monitoring LTE-M uplink/downlink frequency bands, adjacent or adjacent sector signal carrier-to-interference ratio C/I in real time, carrying out uninterrupted frequency sweep test on the whole subway line wireless network, caching received wireless signal level, carrier-to-noise ratio and channel bandwidth, and transmitting the wireless signal level, carrier-to-interference ratio and channel bandwidth to a subway intranet through a remote data transmission module;
the vehicle-mounted monitoring small station is arranged on a vehicle and used for connecting with a train position information interface, binding a wireless measurement index and measurement position information of a measurement point by combining a position calibration module, and transmitting data to the monitoring signal intelligent analysis software through an IP data network;
the monitoring signal intelligent analysis software is used for developing a wireless system intelligent analysis algorithm through a wireless air interface, a convergence network port and a log port, and further utilizing the big data analysis of the time-frequency characteristics of a data packet to realize the online monitoring of the CBTC wireless signal coverage quality of the subway station, and identifying a typical interference pattern and positioning the source of an interference signal by measuring the signal strength RSSI of the interference signal and combining an ID3 decision tree algorithm;
the decision tree algorithm comprises the following steps:
step 1: carrying out data initialization on the interference signal sample and the preprocessed characteristic parameter sample;
step 2: randomly selecting a part of samples from the collected interference signal samples as a training set, and using the other part of samples as a test set;
and step 3: using training set to create decision tree classifier, calculating information gain of base station identification code, up/down voice quality, voice transmission time, signal carrier-to-interference ratio C/I, whether there is third order intermodulation relation and bottom noise rise value, the expression of the information gain is shown in formula (1):
wherein, P i Is the probability of the ith class, S is the set of samples,
the probability that the number of jth class of feature A accounts for all classes of feature A, P ij The probability of occupying different classes of the sample set S in the jth class under the characteristic A is obtained;
and 4, step 4: arranging the information gains of the characteristic parameters according to the magnitude sequence as the sequence for identifying the interference signals;
and 5: fitting the established decision tree by using a test set to obtain an identification rate graph of the interference pattern;
step 6: establishing a loss function of the whole minimized decision tree through cross validation of error values of a training set and a test set, and finding out the maximum pruning level of the minimum error;
and 7: and pruning the established decision tree according to the maximum pruning level to prevent the decision tree from being over-fitted.
2. The intelligent rail transit vehicle-ground communication monitoring system according to claim 1, further comprising a tunnel positioning module, wherein the tunnel positioning module is installed in a subway tunnel and used for assisting the vehicle-mounted monitoring small station in determining current train position information.
3. The intelligent monitoring system for wireless communication of the rail transit vehicle-ground according to claim 1, further comprising an electromagnetic environment management module, wherein the electromagnetic environment management module is installed along the subway and is used for monitoring the coverage and interference condition of electromagnetic signals along the subway.
4. The intelligent monitoring system for rail transit vehicle-ground wireless communication as claimed in claim 1, wherein the frequency sweep mode frequency band range of the fixed monitoring small station is a continuous rail transit wireless communication frequency band.
5. The system according to claim 1, wherein the frequency band of the monitoring mode of the fixed monitoring station is a discontinuous frequency band available for the monitored system.
6. The intelligent monitoring system for rail transit vehicle-ground wireless communication as claimed in claim 1, wherein all test data recorded in the vehicle-mounted monitoring small station comprise measurement time and measurement position, and can be derived in a common format and remotely accessed into the intelligent monitoring signal analysis software.
7. The system according to claim 1, wherein the vehicle-mounted small monitoring station has static and dynamic data management functions of a subway wireless system and can perform wireless interference analysis.
8. The intelligent monitoring system for rail transit vehicle-ground wireless communication as claimed in claim 1, wherein the vehicle-mounted monitoring small station is equipped with a high-pointing interference source positioning antenna.
9. A decision tree method in an intelligent wireless communication monitoring system of a rail transit vehicle, which is characterized in that the intelligent wireless communication monitoring system of the rail transit vehicle is used for monitoring according to any one of claims 1 to 8, and comprises the following steps:
step 1: carrying out data initialization on the interference signal sample and the preprocessed characteristic parameter sample;
step 2: randomly selecting a part of samples from the collected interference signal samples as a training set, and using the other part of samples as a test set;
and step 3: a decision tree classifier is created by utilizing a training set, and information gain of base station identification codes, uplink/downlink voice quality, voice transmission time, signal carrier-to-interference ratio C/I, the presence or absence of a third-order intermodulation relation and a bottom noise rise value is calculated, wherein the information gain expression is shown in a formula (1):
wherein, P i Is the probability of the ith class, S is the set of samples,
the probability that the number of jth class of feature A accounts for all classes of feature A, P ij The probability of occupying different classes of the sample set S in the jth class under the characteristic A is obtained;
and 4, step 4: arranging the information gains of the characteristic parameters according to the magnitude sequence as the sequence for identifying the interference signals;
and 5: fitting the established decision tree by using a test set to obtain an identification rate graph of the interference pattern;
step 6: establishing a loss function of the whole minimized decision tree through cross validation of error values of a training set and a test set, and finding out the maximum pruning level of the minimum error;
and 7: and pruning the established decision tree according to the maximum pruning level to prevent the decision tree from being over-fitted.
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