CN116390137A - Intelligent terminal wireless network signal abnormity monitoring method - Google Patents
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Abstract
The invention discloses an intelligent terminal wireless network signal abnormity monitoring method, relates to the technical field of signal monitoring, and solves the technical problems that the wireless network signal cannot be monitored permanently and effectively in the prior art, influencing factors are difficult to locate, and operation and maintenance personnel cannot remove faults in time; the method is based on a time window analysis signal test curve, and when the test time is stable in the time window and no local mutation occurs, the wireless network signal corresponding to the time window is judged to have abnormality; avoiding invalid judgment while guaranteeing the monitoring precision of wireless network signals; the method analyzes the consistency of the signal curve segment and the environment curve segment of the abnormal time window, extracts signal influence factors according to the consistency analysis result, and determines the signal influence weight of each signal influence factor according to the integral state of the first derivative; the operation and maintenance personnel can rapidly extract the environment sub-data affecting the wireless network signal according to the signal affecting sequence, and simultaneously perform high-efficiency processing.
Description
Technical Field
The invention belongs to the field of signal monitoring, relates to a signal anomaly monitoring technology of an intelligent terminal, and particularly relates to a wireless network signal anomaly monitoring method of the intelligent terminal.
Background
Intelligent terminals are a type of embedded computer system device that includes hardware structures and software structures. The intelligent terminal can autonomously perform data acquisition, data processing and data transmission through the built-in controller, and has the characteristics of high integration level, small volume, high performance and the like, and has wide application scenes.
One of the most important functions of the intelligent terminal is to perform data transmission, but since the device position of the intelligent terminal is not fixed, the data transmission cannot be performed in a limited manner, and most of the data transmission is completed in a wireless manner, so that the abnormal monitoring of the wireless network information is very important. In the prior art, test data is generally sent to an intelligent terminal through a server, and whether a wireless network signal is abnormal or not is judged according to the round trip time of the test data. In the prior art, when abnormal monitoring is carried out on a wireless network signal of an intelligent terminal, the wireless network signal cannot be permanently and effectively monitored by judging a few round trip times of test data, and influencing factors are difficult to locate, so that operation and maintenance personnel cannot timely remove faults; therefore, a method for monitoring wireless network signal abnormality of an intelligent terminal is needed.
Disclosure of Invention
The present invention aims to solve at least one of the technical problems existing in the prior art; therefore, the invention provides an intelligent terminal wireless network signal abnormity monitoring method, which is used for solving the technical problems that the wireless network signal cannot be permanently and effectively monitored, influencing factors are difficult to locate, and operation and maintenance personnel cannot timely remove faults in the prior art.
In order to achieve the above object, a first aspect of the present invention provides a method for monitoring signal anomalies in a wireless network of an intelligent terminal, including:
the method comprises the steps of regularly and continuously sending test data to the intelligent terminal, completing a test period when the test data returned by the intelligent terminal are received, counting the corresponding duration of the test period and marking the duration as test time;
constructing a signal test curve by taking the mark of the test period as an independent variable and the corresponding test time as the dependent variable; constructing time windows based on the identifiers of the test periods, and analyzing whether the signal test curves are abnormal in each time window;
acquiring environment data corresponding to a plurality of intelligent terminals, and constructing a plurality of signal environment curves by taking the identification of a test period as an independent variable and taking the environment data as a dependent variable; and extracting signal influence factors of the abnormal time window and corresponding signal influence weights based on the plurality of signal environment curves.
Preferably, the constructing time windows based on the identification of the test period, analyzing whether the signal test curve is abnormal in each time window, includes:
setting a time window according to the working strength of the intelligent terminal; wherein, the larger the time window is, the smaller the working strength is;
extracting a signal curve segment corresponding to the time window from the signal test curve; calculating a test time mean value in the signal curve segment and a mean square error of the corresponding first derivative, wherein the test time mean value and the mean square error are respectively marked as CSJ and DJC;
the abnormality evaluation coefficient YPX is calculated by the formula ypx=α×csj×exp (DJC); wherein, alpha is a proportionality coefficient set according to experience, the default value is 1, exp () is an index based on a natural number e;
when the abnormality evaluation coefficient YPX is larger than the corresponding abnormality evaluation threshold, determining that the signal curve segment is abnormal; otherwise, judging that the signal curve segment is normal; wherein the abnormality evaluation threshold is empirically set.
Preferably, the obtaining environmental data corresponding to the plurality of intelligent terminals, using the identifier of the test period as an independent variable, and using the environmental data as the independent variable to construct a plurality of signal environmental curves includes:
the intelligent terminal returns the environment data and the test data together and extracts environment sub-data in the environment data; wherein the environmental data includes temperature, humidity, wind force or air pressure;
constructing a signal environment curve by taking the mark of the test period as an independent variable and the corresponding environment sub-data as the dependent variable; wherein, an intelligent terminal corresponds at least one signal environment curve.
Preferably, when the signal curve segment is abnormal, extracting a signal environment curve according to a corresponding time window to obtain a plurality of environment curve segments;
judging whether the change trend of the signal curve segment is consistent with the change trend of the environment curve segment; if yes, marking the corresponding environment sub-data as a signal influencing factor; and if not, judging the next environmental curve segment.
Preferably, after determining the signal influencing factor, calculating the signal influencing weight of the signal influencing factor includes:
marking the first derivative mean square error of the corresponding environmental curve segment as HJC;
obtaining a signal influence weight XYQ corresponding to the signal influence factor through the formula XYQ=beta×exp (HJC)/exp (DJC); wherein, beta is a proportionality coefficient which is set according to experience, and a default value is 1.
Preferably, integrating the signal influence factors corresponding to the intelligent terminals and the corresponding signal influence weights to generate a signal influence sequence; and the operation and maintenance personnel conduct investigation and treatment on the environment sub-data causing the abnormal wireless network signals of the intelligent terminal according to the signal influence sequence.
The second aspect of the invention provides an intelligent terminal wireless network signal abnormality monitoring system, which is used for realizing an intelligent terminal wireless network signal abnormality monitoring method, and comprises a central control module and a plurality of intelligent terminals which are in communication connection with the central control module;
the method comprises the steps that test data are sent to a plurality of intelligent terminals through a central control module at regular time, and the intelligent terminals immediately return to the central control module after receiving the test data;
the central control module acquires test time after receiving test data returned by the intelligent terminal, and builds a signal test curve based on the test time; judging whether the wireless network signal of the corresponding intelligent terminal is abnormal or not based on the signal test curves, and summarizing signal influence factors according to the signal test curves.
The third aspect of the invention provides an intelligent terminal wireless network signal abnormality monitoring device, which comprises a storage medium and a processor; the storage medium stores operation instructions, and the processor executes the operation instructions to realize an intelligent terminal wireless network signal abnormality monitoring method.
Compared with the prior art, the invention has the beneficial effects that:
1. according to the method, the test data are sent to the intelligent terminal in a timed and continuous mode, the test time corresponding to a plurality of test periods is obtained, and then a signal test curve is generated; analyzing a signal test curve based on a time window, and judging that the wireless network signal corresponding to the time window is abnormal when the test time is stable in the time window and no local mutation occurs; and analyzing the test time in the time period, and avoiding invalid judgment while guaranteeing the monitoring precision of the wireless network signal.
2. The method comprises the steps of analyzing the consistency of a signal curve segment of an abnormal time window and an environment curve segment, extracting signal influence factors according to a consistency analysis result, determining the signal influence weight of each signal influence factor according to the integral state of a first derivative, and transmitting a generated signal influence sequence to operation and maintenance personnel; the operation and maintenance personnel can rapidly extract environment sub-data affecting the wireless network signals according to the signal affecting sequence, and meanwhile, the environment sub-data are efficiently processed to ensure the strength of the wireless network signals of the intelligent terminal.
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In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic diagram of the working steps of the present invention;
fig. 2 is a schematic diagram of the system principle of the present invention.
Detailed Description
The technical solutions of the present invention will be clearly and completely described in connection with the embodiments, and it is obvious that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
In the prior art, when abnormal monitoring is performed on a wireless network signal of an intelligent terminal, the wireless network signal cannot be continuously and effectively monitored only by judging a few round trip times of test data, and the round trip time is generally directly compared with a time threshold value, so that continuous monitoring on the state of the wireless network signal is difficult to realize, and invalid judgment is easily caused by instantaneous environmental influence factors.
Referring to fig. 1, an embodiment of a first aspect of the present invention provides a method for monitoring signal anomalies in a wireless network of an intelligent terminal, including: the method comprises the steps of regularly and continuously sending test data to the intelligent terminal, completing a test period when the test data returned by the intelligent terminal are received, counting the corresponding duration of the test period and marking the duration as test time; constructing a signal test curve by taking the mark of the test period as an independent variable and the corresponding test time as the dependent variable; constructing time windows based on the identifiers of the test periods, and analyzing whether the signal test curves are abnormal in each time window; acquiring environment data corresponding to a plurality of intelligent terminals, and constructing a plurality of signal environment curves by taking the identification of a test period as an independent variable and taking the environment data as a dependent variable; and extracting signal influence factors of the abnormal time window and corresponding signal influence weights based on the plurality of signal environment curves.
According to the method, the test data are sent to the intelligent terminal in a timed and continuous mode, the test time corresponding to a plurality of test periods is obtained, and then a signal test curve is generated; analyzing a signal test curve based on a time window, and judging that the wireless network signal corresponding to the time window is abnormal when the test time is stable in the time window and no local mutation occurs; and analyzing the test time in the time period, and avoiding invalid judgment while guaranteeing the monitoring precision of the wireless network signal.
The method comprises the steps of analyzing the consistency of a signal curve segment of an abnormal time window and an environment curve segment, extracting signal influence factors according to a consistency analysis result, determining the signal influence weight of each signal influence factor according to the integral state of a first derivative, and transmitting a generated signal influence sequence to operation and maintenance personnel; the operation and maintenance personnel can rapidly extract environment sub-data affecting the wireless network signals according to the signal affecting sequence, and meanwhile, the environment sub-data are efficiently processed to ensure the strength of the wireless network signals of the intelligent terminal.
The invention constructs time windows based on the identification of the test period, analyzes whether the signal test curve is abnormal in each time window, and comprises the following steps: setting a time window according to the working strength of the intelligent terminal; extracting a signal curve segment corresponding to the time window from the signal test curve; calculating a test time mean value in the signal curve segment and a mean square error of the corresponding first derivative, wherein the test time mean value and the mean square error are respectively marked as CSJ and DJC; the abnormality evaluation coefficient YPX is calculated by the formula ypx=α×csj×exp (DJC); when the abnormality evaluation coefficient YPX is larger than the corresponding abnormality evaluation threshold, determining that the signal curve segment is abnormal; otherwise, judging that the signal curve segment is normal.
The time window is reasonably set according to the working strength of the intelligent terminal. When the working intensity corresponding to the intelligent terminal is smaller, the time window is larger; if the working strength of the intelligent terminal tends to be stable, the time windows are set at equal intervals. Each test period corresponds to a test time, and the larger the test time is, the more smooth the corresponding communication process is, and the larger the continuous test time is, the more the wireless network signal is possibly affected. It should be noted that if the returned test data is not received in one test period, the corresponding test time is set to a sufficiently large value, so that the later analysis is facilitated.
When the wireless network signal is analyzed according to the signal test curve, the invention mainly considers the variation amplitude of the test time in the time window, namely, the variation amplitude is expressed by the mean value of the test time and the mean square error of the first derivative of the signal test curve. The smaller the mean value of the test time, the better the wireless network signal, the smaller the mean square error of the first derivative, and the smaller the variation amplitude of the wireless network signal. The two are combined to comprehensively evaluate the state of the wireless network signal in the time window, so that misjudgment caused by a single factor is avoided.
The invention obtains environment data corresponding to a plurality of intelligent terminals, takes the identification (expressed by positive integer) of a test period as an independent variable, and constructs a plurality of signal environment curves by taking the environment data as the dependent variable, comprising the following steps: the intelligent terminal returns the environment data and the test data together and extracts environment sub-data in the environment data; and constructing a signal environment curve by taking the identification of the test period as an independent variable and the corresponding environment sub-data as the dependent variable.
The environmental data comprises environmental data such as temperature, humidity, wind power or air pressure which influence the wireless network signals, and the change trend of the environmental data and the environmental data is consistent when the environmental data influence the wireless signals. If the air pressure is larger, the wireless network signal is smaller, and the consistency of the wireless network signal and the wireless network signal is opposite; the consistency here does not include merely increasing or decreasing consistently. It should be noted that, because the environment data includes a plurality of environment sub-data, when a signal environment curve is established, the same intelligent terminal corresponds to a plurality of signal environment curves.
When the signal curve segments are abnormal, extracting a signal environment curve according to a corresponding time window to obtain a plurality of environment curve segments; judging whether the change trend of the signal curve segment is consistent with the change trend of the environment curve segment; if yes, marking the corresponding environment sub-data as a signal influencing factor; and if not, judging the next environmental curve segment.
If the signal curve segment is abnormal, whether the change trend of the signal curve segment is consistent with the change trend of each environmental curve segment (the signal environmental curve intercepted according to the time window) is analyzed, if the signal curve segment is in an ascending trend, the ascending or descending of the environmental curve segment can be understood as the consistent change trend. When the variation trend is consistent, the environment sub-data corresponding to the corresponding environment curve segment is marked as a signal influencing factor.
It should be noted that the corresponding signal influencing factors are the same in each time window, i.e. some environmental sub-data influence a certain time window, but not another time window, so that all environmental curve segments need to be analyzed in each time window.
In a preferred embodiment, after determining the signal influencing factor, calculating the signal influencing weight of the signal influencing factor comprises: marking the first derivative mean square error of the corresponding environmental curve segment as HJC; the signal influence weight XYQ corresponding to the signal influence factor is obtained by the formula xyq=β×exp (HJC)/exp (DJC).
The degree of consistent variation trend is mainly considered in the calculation formula of the signal influence weight, and the closer the first derivative mean square error of the two curve segments is, the higher the correlation degree of the variation trend of the two curve segments is, and the larger the corresponding signal influence weight is.
Integrating signal influence factors corresponding to the intelligent terminal and corresponding signal influence weights to generate a signal influence sequence; and the operation and maintenance personnel conduct investigation and treatment on the environment sub-data causing the abnormal wireless network signals of the intelligent terminal according to the signal influence sequence. And the operation and maintenance personnel begin to examine from the signal influence factor with the largest signal influence weight, so that the problem of signal abnormality of the intelligent terminal wireless network is gradually solved.
Referring to fig. 2, a second aspect of the present invention provides an intelligent terminal wireless network signal anomaly monitoring system, configured to implement an intelligent terminal wireless network signal anomaly monitoring method, including a central control module, and a plurality of intelligent terminals communicatively connected to the central control module; the method comprises the steps that test data are sent to a plurality of intelligent terminals through a central control module at regular time, and the intelligent terminals immediately return to the central control module after receiving the test data; the central control module acquires test time after receiving test data returned by the intelligent terminal, and builds a signal test curve based on the test time; judging whether the wireless network signal of the corresponding intelligent terminal is abnormal or not based on the signal test curves, and summarizing signal influence factors according to the signal test curves.
The central control module is mainly controlled by the processor to process data, and is in communication connection with a plurality of intelligent terminals. When a certain intelligent terminal is used as a target, the central control module can only continuously send test data to the intelligent terminal; and when the intelligent terminals in a certain range are targeted, the central control module can send test data to a plurality of intelligent terminals in the range at the same time. The central control module can also communicate with the operation and maintenance personnel in time, and sends the processing result to the operation and maintenance personnel.
An embodiment of a third aspect of the present invention provides an intelligent terminal wireless network signal anomaly monitoring device, including a storage medium and a processor; the storage medium stores operation instructions, and the processor executes the operation instructions to realize an intelligent terminal wireless network signal abnormality monitoring method. It should be noted that, executing the operation instruction by the processor controls an intelligent terminal wireless network signal abnormality monitoring system to operate, and after the system operates, an intelligent terminal wireless network signal abnormality monitoring method is implemented.
The partial data in the formula are all obtained by removing dimension and taking the numerical value for calculation, and the formula is a formula closest to the real situation obtained by simulating a large amount of collected data through software; the preset parameters and the preset threshold values in the formula are set by those skilled in the art according to actual conditions or are obtained through mass data simulation.
The working principle of the invention is as follows:
counting the corresponding duration of the test period and marking the duration as test time; constructing a signal test curve by taking the mark of the test period as an independent variable and the corresponding test time as the dependent variable; and constructing time windows based on the identification of the test period, and analyzing whether the signal test curve is abnormal in each time window.
Acquiring environment data corresponding to a plurality of intelligent terminals, and constructing a plurality of signal environment curves by taking the identification of a test period as an independent variable and taking the environment data as a dependent variable; and extracting signal influence factors of the abnormal time window and corresponding signal influence weights based on the plurality of signal environment curves.
The above embodiments are only for illustrating the technical method of the present invention and not for limiting the same, and it should be understood by those skilled in the art that the technical method of the present invention may be modified or substituted without departing from the spirit and scope of the technical method of the present invention.
Claims (8)
1. The method for monitoring the wireless network signal abnormality of the intelligent terminal is characterized by comprising the following steps of:
the method comprises the steps of regularly and continuously sending test data to the intelligent terminal, completing a test period when the test data returned by the intelligent terminal are received, counting the corresponding duration of the test period and marking the duration as test time;
constructing a signal test curve by taking the mark of the test period as an independent variable and the corresponding test time as the dependent variable; constructing time windows based on the identifiers of the test periods, and analyzing whether the signal test curves are abnormal in each time window;
acquiring environment data corresponding to a plurality of intelligent terminals, and constructing a plurality of signal environment curves by taking the identification of a test period as an independent variable and taking the environment data as a dependent variable; and extracting signal influence factors of the abnormal time window and corresponding signal influence weights based on the plurality of signal environment curves.
2. The method for monitoring signal anomalies of wireless network of intelligent terminal according to claim 1, wherein the constructing time windows based on the identification of the test period, analyzing whether the signal test curve is anomalous in each time window, comprises:
setting a time window according to the working strength of the intelligent terminal; wherein, the larger the time window is, the smaller the working strength is;
extracting a signal curve segment corresponding to the time window from the signal test curve; calculating a test time mean value in the signal curve segment and a mean square error of the corresponding first derivative, wherein the test time mean value and the mean square error are respectively marked as CSJ and DJC;
the abnormality evaluation coefficient YPX is calculated by the formula ypx=α×csj×exp (DJC); wherein, alpha is a proportionality coefficient set according to experience, the default value is 1, exp () is an index based on a natural number e;
when the abnormality evaluation coefficient YPX is larger than the corresponding abnormality evaluation threshold, determining that the signal curve segment is abnormal; otherwise, judging that the signal curve segment is normal; wherein the abnormality evaluation threshold is empirically set.
3. The method for monitoring signal anomalies of wireless network of intelligent terminals according to claim 2, wherein the obtaining environmental data corresponding to the plurality of intelligent terminals, using the identification of the test period as an independent variable, and using the environmental data as an independent variable, constructs a plurality of signal environmental curves, includes:
the intelligent terminal returns the environment data and the test data together and extracts environment sub-data in the environment data; wherein the environmental data includes temperature, humidity, wind force or air pressure;
constructing a signal environment curve by taking the mark of the test period as an independent variable and the corresponding environment sub-data as the dependent variable; wherein, an intelligent terminal corresponds at least one signal environment curve.
4. The method for monitoring signal abnormality of intelligent terminal wireless network according to claim 3, wherein when signal curve segments are abnormal, extracting signal environment curves according to corresponding time windows to obtain a plurality of environment curve segments;
judging whether the change trend of the signal curve segment is consistent with the change trend of the environment curve segment; if yes, marking the corresponding environment sub-data as a signal influencing factor; and if not, judging the next environmental curve segment.
5. The method for monitoring signal anomalies in a wireless network of an intelligent terminal according to claim 4, wherein after determining the signal influencing factors, calculating the signal influencing weights of the signal influencing factors, comprises:
marking the first derivative mean square error of the corresponding environmental curve segment as HJC;
obtaining a signal influence weight XYQ corresponding to the signal influence factor through the formula XYQ=beta×exp (HJC)/exp (DJC); wherein, beta is a proportionality coefficient which is set according to experience, and a default value is 1.
6. The method for monitoring signal anomalies of a wireless network of an intelligent terminal according to claim 5, wherein the signal influencing factors corresponding to the intelligent terminal and the corresponding signal influencing weights are integrated to generate a signal influencing sequence; and the operation and maintenance personnel conduct investigation and treatment on the environment sub-data causing the abnormal wireless network signals of the intelligent terminal according to the signal influence sequence.
7. An intelligent terminal wireless network signal abnormity monitoring system for realizing the intelligent terminal wireless network signal abnormity monitoring method as defined in any one of claims 1 to 6, which is characterized by comprising a central control module and a plurality of intelligent terminals in communication connection with the central control module;
the method comprises the steps that test data are sent to a plurality of intelligent terminals through a central control module at regular time, and the intelligent terminals immediately return to the central control module after receiving the test data;
the central control module acquires test time after receiving test data returned by the intelligent terminal, and builds a signal test curve based on the test time; judging whether the wireless network signal of the corresponding intelligent terminal is abnormal or not based on the signal test curves, and summarizing signal influence factors according to the signal test curves.
8. The intelligent terminal wireless network signal abnormality monitoring device is characterized by comprising a storage medium and a processor; the storage medium stores operation instructions, and the processor executes the operation instructions to implement the method for monitoring abnormal signals of the wireless network of the intelligent terminal according to any one of claims 1 to 6.
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