CN108990089B - Multi-detection window joint detection analysis method for mobile communication network - Google Patents
Multi-detection window joint detection analysis method for mobile communication network Download PDFInfo
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Abstract
The invention discloses a multi-detection-window joint detection analysis method of a mobile communication network, which is used for carrying out joint detection on multiple detection windows, establishing automatic adaptive association through the correlation strength and weakness relation among the detection windows, and triggering alarm reminding or quickly diagnosing the reason of an abnormal event before the abnormal event occurs. The method is suitable for various comprehensive process evaluation from quantitative change to qualitative change, early analysis and early warning of abnormal events and reason analysis of the abnormal events (such as wireless signals, air temperature, air pressure, humidity, pollution indexes, noise, pavement flatness and the like), and particularly highlights the advance warning from shallow layer correlation to deep layer correlation quantitative change until the qualitative change of the abnormal events and the correlation backtracking analysis of the event reasons after the abnormal events so as to solve the automatic correlation intelligent analysis of test data. The technical scheme has the characteristics of considering the convenience of programming at the beginning, being convenient and rapid to deploy, greatly improving the precision and efficiency of system analysis, having wide application range and the like.
Description
Technical Field
The invention relates to the technical field of wireless network communication analysis, in particular to a multi-detection-window joint detection and analysis method for a mobile communication network.
Background
With the rapid development of high speed, high speed rail and motor cars, mileage is rapidly increasing. Mobile operators strive to provide wireless network communication service for passengers along the way, and higher requirements are put on the network with the coming of automatic driving in the future; the special network significance of high-speed rails and motor trains is more different, and the train dispatching and control information adopts a wireless communication system, which is directly related to the safety of railway operation.
At present, when analyzing and evaluating wireless network coverage of linear routes such as highways, railways and the like, quality, events and other problems domestically and abroad, a plurality of statistical indexes are adopted to respectively describe wireless conditions along the road sections in a general way for the whole test route, wherein the abnormal events and the quality defect problems are completely analyzed manually, but the abilities and the working attitude of an analyst are uncertain, and objective quantitative measurement is lacked, so that the analysis precision is insufficient, even misjudgment is caused. Meanwhile, the existing industry assessment system can not meet the fine requirements, such as: test and assessment standards of China Mobile group; railway industry standard 'railway digital mobile communication system (GSM-R) engineering detection regulation' and 'comprehensive network management system network performance index statistical table'.
Accordingly, it is necessary to analyze this situation in depth to develop a solution with higher accuracy and efficiency.
Disclosure of Invention
The invention aims to provide a multi-detection-window joint detection and analysis method for a mobile communication network, which can greatly improve the analysis precision and efficiency of various types of wireless communication systems.
The purpose of the invention is realized by the following technical scheme:
a multi-detection window joint detection analysis method for a mobile communication network comprises the following steps:
completely analyzing data in a field test Log file to obtain two parts of signaling data and measurement data;
carrying out logic separation on data in different periods and random sampling, and dividing signaling data and measurement data into the following parts according to different attributes: abnormal events, strong correlation factors and weak correlation factors; the weak correlation factor is converted into a strong correlation factor with a certain probability after lasting for a certain time, and the strong correlation factor is converted into an abnormal event with a certain probability after lasting for a certain time;
the abnormal event triggers a timely window to realize the reason diagnosis of the abnormal event; triggering a weak correlation detection window by the weak correlation factors, predicting the weak correlation factors, analyzing the relationship between the weak correlation factors and the strong correlation factors, and triggering a primary alarm; the strong correlation factor triggers a strong correlation detection window, predicts the strong correlation factor and analyzes the relationship with the weak correlation factor, and simultaneously triggers a secondary alarm.
According to the technical scheme provided by the invention, the multi-detection-window joint detection is realized by adopting an analysis method of a multi-detection-window joint detection algorithm for the mobile communication network, the automatic adaptation association is established through the correlation strength relation between the detection windows, and the alarm reminding is triggered or the reason of the abnormal event is rapidly diagnosed before the abnormal event occurs to the maximum extent. The technical scheme is designed by considering the convenience of programming at the beginning, is convenient and rapid to deploy, greatly improves the precision and efficiency of system analysis, and is particularly suitable for daily comprehensive analysis.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on the drawings without creative efforts.
Fig. 1 is a schematic diagram of a method for joint detection and analysis of multiple detection windows in a mobile communication network according to an embodiment of the present invention;
fig. 2 is a schematic diagram illustrating a correlation strength relationship between mobile communication signals passing through a detection window according to an embodiment of the present invention;
fig. 3 is a schematic diagram illustrating a relationship between various factors and an abnormal event according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention are clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present invention without making any creative effort, shall fall within the protection scope of the present invention.
The embodiment of the invention provides a multi-detection-window joint detection and analysis method for a mobile communication network, which is used for carrying out joint detection on multiple detection windows, establishing automatic adaptive association through the correlation strength relation between the detection windows, and triggering alarm reminding or quickly diagnosing the reason of an abnormal event before the abnormal event occurs. The technical scheme has the characteristics of considering the convenience of programming at the beginning, being convenient and rapid to deploy, greatly improving the precision and efficiency of system analysis, having wide application range and the like.
As shown in fig. 2, a schematic diagram of a method for joint detection and analysis of multiple detection windows in a mobile communication network according to an embodiment of the present invention mainly includes:
and A, analyzing a test Log file and carding a structure.
And A1, completely analyzing the data in the field test Log file to obtain two parts of signaling data and measurement data.
In the embodiment of the invention, the longitude and latitude of the signaling data are correspondingly synchronized, and the measurement data are correspondingly synchronized with the time.
Step A2, logically separating the data of different periods from the random sampling, and dividing the data into signaling data and measurement data according to different attributes: abnormal events, strong correlation factors and weak correlation factors.
In the embodiment of the invention, strong and weak related factors exist in the measured data, and the signaling data is used for judging whether an abnormal event exists.
As shown in fig. 2-3, the introduction of wireless signal interruption is often accompanied by the transition of the weakly correlated feature to the strongly correlated feature until an abnormal event occurs; namely, the weak correlation factor (also called indirect reason) is converted into a strong correlation factor with a certain probability after lasting for a certain time, and the strong correlation factor (also called direct reason) is converted into an abnormal event with a certain probability after lasting for a certain time; the detection window is the range interval of strong correlation, weak correlation and abnormal events. The period 480ms in fig. 3 refers to an event interval of sending periodic measurement reports of the GSMR wireless system, and the system confirms that a wireless channel is available through uninterrupted measurement.
Illustratively, the weak correlation factor includes the magnitude of the coverage level, the strong correlation factor includes the magnitude of the signal-to-noise ratio, and the like, the reference index of the abnormal event may be the magnitude of the bit error rate, and the abnormal event that may be caused mainly includes: access failure, too large time delay of channel allocation, call reestablishment, rescue switching, switching failure, channel coding degradation, abnormal release and release time delay.
Step A3, after dividing the measurement data according to different attributes, dividing the respective analysis areas according to the cause-effect relationship (the cause of the strong and weak correlation factors, and the effect of the abnormal event) between the abnormal events and the triggering judgment time length. The trigger judgment duration here can be understood as the duration of the corresponding strong and weak correlation factors, and the analysis area is also the detection range.
In the embodiment of the invention, the abnormal event triggers a timely window to realize the reason diagnosis of the abnormal event (step B); triggering a weak correlation detection window by the weak correlation factors, predicting the weak correlation factors and analyzing the relationship with the strong correlation factors (turning to the step D), and triggering a first-level alarm; the strong correlation factor triggers the strong correlation detection window, the strong correlation factor is predicted, the relation with the weak correlation factor is analyzed (step C is carried out), and meanwhile, a secondary alarm is triggered.
And step B, triggering a timely window by the abnormal event to realize the reason diagnosis of the abnormal event.
And step B1, marking the abnormal events triggering the timely window, and carrying out backtracking analysis to determine the correlation between the strong correlation factors and the weak correlation factors.
And step B2, triggering backtracking analysis of the strong correlation factors, thereby analyzing the relationship between the abnormal event and the strong correlation factors, and correcting the corresponding correlation model according to the analysis result.
And step B3, triggering backtracking analysis of the weak correlation factors, thereby analyzing the relationship between the abnormal event and the weak correlation factors, and correcting the corresponding correlation model according to the analysis result.
And C, triggering a strong correlation detection window by the strong correlation factors, predicting the strong correlation factors and analyzing the relationship between the strong correlation factors and the weak correlation factors.
And step C1, determining whether the current strong correlation factor triggers a strong correlation detection window based on the judgment model, if so, performing statistical analysis on the current strong correlation factor and the correlation model, triggering a secondary alarm, and simultaneously performing the step C2 and the step C3 to perform abnormal event early warning analysis and weak correlation factor backtracking analysis.
And step C2, judging the possibility of abnormal events caused by the current strong correlation factors, and taking the judgment result as the record of the correlation model learning.
And C3, backtracking the deviation of the weak correlation factors and the corresponding thresholds, and deducing the result to be used as a record of the correlation model learning.
In the embodiment of the invention, the correlation model is corrected by combining the recorded result.
And D, triggering a weak correlation detection window by the weak correlation factors, predicting the weak correlation factors and analyzing the relationship between the weak correlation factors and the strong correlation factors.
And D1, determining whether the current weak correlation factor triggers a weak correlation detection window or not based on the judgment model, if so, performing statistical analysis on the current weak correlation factor and the correlation model, triggering a primary alarm, and simultaneously performing the steps D2 and D3 to perform abnormal event early warning analysis and strong correlation factor early warning analysis.
And D2, judging the possibility of abnormal events caused by the current weak correlation factors, and taking the judgment result as the record of the correlation model learning.
And D3, deducing the strong correlation factors and the deviation of the corresponding threshold, and using the deduced result as the record of the correlation model learning.
In the embodiment of the invention, the correlation model is corrected by combining the recorded result. In addition, the step D3 is actually a probabilistic relationship analysis for predicting weak correlation factors (indirect causes) triggering thresholds (i.e. decision models) and predicting abnormal events.
Those skilled in the art can understand that the decision model involved when the strong and weak correlation factors trigger the corresponding detection windows needs to be adjusted according to the surrounding environment of the segment to be analyzed, and the complexity of the surrounding environment and the size of the threshold value in the interference degree image decision model.
The technical scheme of the embodiment of the invention is suitable for various comprehensive process evaluation from quantitative change to qualitative change, early-stage analysis and early warning of abnormal events and reason analysis (such as wireless signals, air temperature, air pressure, humidity, pollution indexes, noise, pavement flatness and the like) of the abnormal events, is used for solving the problem of automatic correlation intelligent analysis of test data and greatly improves the analysis precision and the working efficiency; in particular, the method highlights the early warning from shallow correlation to deep correlation quantitative change until the quality of the abnormal event changes and the event reason of the correlation backtracking analysis after the abnormal event.
Through the above description of the embodiments, it is clear to those skilled in the art that the above embodiments can be implemented by software, and can also be implemented by software plus a necessary general hardware platform. With this understanding, the technical solutions of the embodiments can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a usb disk, a removable hard disk, etc.), and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods according to the embodiments of the present invention.
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 (6)
1. A method for joint detection and analysis of multiple detection windows in a mobile communication network is characterized by comprising the following steps:
completely analyzing data in a field test Log file to obtain two parts of signaling data and measurement data;
carrying out logic separation on data in different periods and random sampling, and dividing signaling data and measurement data into the following parts according to different attributes: abnormal events, strong correlation factors and weak correlation factors; the weak correlation factor is converted into a strong correlation factor with a certain probability after lasting for a certain time, and the strong correlation factor is converted into an abnormal event with a certain probability after lasting for a certain time;
the abnormal event triggers a timely window to realize the reason diagnosis of the abnormal event; triggering a weak correlation detection window by the weak correlation factors, predicting the weak correlation factors, analyzing the relationship between the weak correlation factors and the strong correlation factors, and triggering a primary alarm; the strong correlation factor triggers a strong correlation detection window, predicts the strong correlation factor and analyzes the relationship with the weak correlation factor, and simultaneously triggers a secondary alarm.
2. The method according to claim 1, wherein the abnormal event comprises: access failure, too large time delay of channel allocation, call reestablishment, rescue switching, switching failure, channel coding degradation, abnormal release and release time delay.
3. The method according to claim 1, wherein after the signaling data and the measurement data are divided according to different attributes, respective analysis areas are further divided according to causal strength relationship between abnormal events and trigger decision duration.
4. The method according to claim 1, wherein the abnormal event triggers a timely window, and the diagnosis of the cause of the abnormal event comprises:
marking the abnormal events triggering the timely window, and carrying out backtracking analysis to determine the correlation between the abnormal events and the strong correlation factors and the weak correlation factors;
and triggering backtracking analysis of the strong relevant factors and the weak relevant factors so as to analyze the relation among the abnormal event, the strong relevant factors and the weak relevant factors and correct the corresponding correlation model according to the analysis result.
5. The method of claim 1, wherein weak correlation factors trigger weak correlation detection windows, and predicting weak correlation factors and analyzing relationships with strong correlation factors comprises:
the weak correlation factors comprise the size of a coverage level, whether the current weak correlation factors trigger a weak correlation detection window or not is determined based on a judgment model, if yes, the current weak correlation factors and the correlation model are subjected to statistical analysis, a primary alarm is triggered, then the possibility of abnormal events caused by the current weak correlation factors is judged, and the judgment result is used as the record of the correlation model learning; meanwhile, deducing the deviation between the strong correlation factor and the corresponding threshold, and using the deduced result as the record of the correlation model learning; and correcting the correlation model by combining the recorded result.
6. The method of claim 1, wherein the strong correlation factor triggers a strong correlation detection window, and predicting the strong correlation factor and analyzing the relationship with the weak correlation factor comprises:
determining whether the current strong correlation factor triggers a strong correlation detection window or not based on a judgment model, if so, performing statistical analysis on the current strong correlation factor and a correlation model, triggering a secondary alarm, then judging the possibility of abnormal events caused by the current strong correlation factor, and taking a judgment result as the learning record of the correlation model; meanwhile, backtracking the deviation of the weak correlation factors and the corresponding threshold, and taking the inference result as the record of the correlation model learning; and correcting the correlation model by combining the recorded result.
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