CN116938696A - Threshold configuration and constellation fault judging method, device, equipment and medium - Google Patents
Threshold configuration and constellation fault judging method, device, equipment and medium Download PDFInfo
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Abstract
The invention relates to a threshold configuration and constellation fault judging method, a device, equipment and a medium, wherein the method comprises the following steps: comprising the following steps: acquiring all detection threshold values set by a data set divided according to fault types in a log, and generating a corresponding type fault history detection threshold baseline after carrying out weighted average on all detection threshold values; predicting a short-term fluctuation trend of a detection threshold baseline according to a data set divided according to fault types in a log; predicting a medium-term fluctuation trend and a long-term fluctuation trend of a detection threshold baseline according to a data set divided according to fault types in a log and the historical detection threshold baseline; determining whether the short-term threshold baseline is changed according to the short-term fluctuation trend of the detection threshold baseline and the short-term warning quantity trend change; determining whether the medium-term threshold baseline and the long-term threshold baseline are changed according to the medium-term fluctuation trend and the medium-term alarm quantity trend change of the detection threshold baseline and the long-term alarm quantity trend; and judging whether the constellation fails according to the changed threshold baseline.
Description
Technical Field
The invention relates to the technical field of world integration, in particular to a method, a device, equipment and a medium for threshold configuration and constellation fault judgment.
Background
In the sky-ground three-dimensional elastic communication network, various network running state information from multiple channels and multiple dimensions of the sky-ground is collected in real time, fusion of multi-source and multi-dimensional information is carried out, an OODA dynamic cognitive ring can be triggered, and finally a decision is generated and executed. The network security event prediction mainly refers to the application of scientific theory, method and existing experience to judge and predict the development trend and hazard condition of the important security event found in the network system, and is an important stage of network security situation awareness, and the main goal of network security situation awareness is to predict the network security event. The current network system has a plurality of services, and the network functions are continuously expanded, so that the security factors influencing the network security situation are more and more, and the problem that the current elastic communication network field needs to be solved is that various complex association relations exist before various factors so that comprehensive perception information is difficult to obtain. For threshold configuration and constellation fault judgment, no solution exists yet.
Disclosure of Invention
Based on the above problems, the present invention provides a method, a device, equipment and a medium for threshold configuration and constellation fault judgment.
In a first aspect, an embodiment of the present invention provides a method for threshold configuration and constellation failure determination, including:
acquiring all detection threshold values set by a data set divided according to fault types in a log, and generating a corresponding type fault history detection threshold baseline after carrying out weighted average on all detection threshold values;
predicting a short-term fluctuation trend of a detection threshold baseline according to a data set divided according to fault types in a log;
predicting a mid-term fluctuation trend of a detection threshold baseline according to a data set divided according to fault types in the log and the historical detection threshold baseline;
predicting a long-term fluctuation trend of a detection threshold baseline according to a data set divided according to fault types in a log and the historical detection threshold baseline;
determining whether the short-term threshold baseline is changed according to the short-term fluctuation trend of the detection threshold baseline and the short-term warning quantity trend change;
determining whether the medium-term threshold baseline is changed according to medium-term fluctuation trend and medium-term alarm quantity trend changes of the detection threshold baseline;
determining whether the long-term threshold baseline is changed according to the long-term fluctuation trend of the detection threshold baseline and the change of the long-term alarm quantity trend;
and judging whether the constellation fails according to the changed threshold baseline.
Further, in the above method for configuring threshold and judging constellation fault, predicting short-term fluctuation trend of the detection threshold baseline according to the data set divided according to fault type in the log includes:
adopting a data set divided according to fault types in a Markov chain prediction model training log, and predicting the probability of abnormality of a detection threshold at the time T+1 through the time T data;
the Markov chain prediction model formula is X (k+1) =X (k) ×P
Wherein X (k) represents a state vector of the trend analysis and prediction object at time t=k, P represents a one-step transition probability matrix, and X (k+1) represents a state vector of the trend analysis and prediction object at time t=k+1.
Further, in the above method for configuring a threshold and determining a constellation fault, predicting a mid-term fluctuation trend of a detection threshold baseline according to a data set divided by fault type in a log and the historical detection threshold baseline includes:
obtaining a value with change of the latest N detection thresholds set by a data set divided according to fault types in a log and adding the currently set value to total N+1 times as denominators; comparing the detected molecular weight with a historical detection threshold baseline one by one, judging that the detected molecular weight is abnormal when the detected molecular weight is higher than the baseline, and otherwise, judging that the detected molecular weight is normal;
and determining the probability of occurrence of abnormality in the middle of the baseline of the detection threshold according to the denominator and the numerator.
Further, in the above method for configuring a threshold and determining a constellation fault, predicting a long-term fluctuation trend of a detection threshold baseline according to a data set divided by fault type in a log and the historical detection threshold baseline includes:
obtaining a value with change of a latest M detection threshold set by a data set divided according to fault types in a log and adding a currently set value to total M+1 times as a denominator; comparing the detected molecular weight with the historical detection threshold baseline one by one, judging that the detected molecular weight is abnormal if the detected molecular weight is higher than the baseline, and otherwise, judging that the detected molecular weight is normal;
and determining the probability of long-term abnormality of the detection threshold baseline according to the denominator and the numerator.
Further, in the above method for configuring a threshold and determining a constellation fault, determining whether the short-term threshold baseline is changed according to the short-term fluctuation trend of the detection threshold baseline and the short-term alarm quantity trend change includes:
and carrying out linkage analysis on the short-term abnormal probability of the detection threshold baseline and the short-term warning quantity trend change to determine whether the short-term threshold baseline is changed.
Further, in the above method for configuring a threshold and determining a constellation fault, determining whether the medium-term threshold baseline is changed according to the medium-term fluctuation trend and the medium-term alarm quantity trend change of the detection threshold baseline includes:
and carrying out linkage analysis on the middle abnormal probability of the detection threshold baseline and the middle alarm quantity trend change to determine whether the middle threshold baseline is changed.
Further, in the above method for configuring a threshold and determining a constellation fault, determining whether the long-term threshold baseline is changed according to the long-term fluctuation trend of the detection threshold baseline and the change of the long-term alarm quantity trend includes:
and carrying out linkage analysis on the long-term abnormal probability of the detection threshold baseline and the trend change of the number of long-term alarms to determine whether the long-term threshold baseline is changed.
In a second aspect, an embodiment of the present invention further provides a threshold configuration and constellation fault determining apparatus, including:
the device comprises an acquisition module and a generation module: the method comprises the steps of obtaining all detection threshold values set by a data set divided according to fault types in a log, carrying out weighted average on all detection threshold values, and generating a corresponding type fault history detection threshold baseline;
a first prediction module: the method comprises the steps of predicting a detection threshold baseline short-term fluctuation trend according to a data set divided according to fault types in a log;
a second prediction module: the method comprises the steps of predicting a mid-term fluctuation trend of a detection threshold baseline according to a data set divided according to fault types in a log and the historical detection threshold baseline;
a third prediction module: the method comprises the steps of predicting a long-term fluctuation trend of a detection threshold baseline according to a data set divided according to fault types in a log and the historical detection threshold baseline;
a first determination module: the method comprises the steps of determining whether a short-term threshold baseline is changed according to short-term fluctuation trend of a detection threshold baseline and short-term alarm quantity trend change;
a second determination module: the medium-term threshold baseline is used for determining whether the medium-term threshold baseline is changed or not according to the medium-term fluctuation trend and the medium-term alarm quantity trend change of the detection threshold baseline;
and a third determination module: the method comprises the steps of determining whether a long-term threshold baseline is changed according to a long-term fluctuation trend of a detection threshold baseline and a long-term alarm quantity trend change;
and a judging module: and judging whether the constellation is faulty or not according to the changed threshold baseline.
In a third aspect, an embodiment of the present invention further provides an electronic device, including: a processor and a memory;
the processor is used for executing any one of the threshold configuration and constellation fault judgment methods by calling the program or the instruction stored in the memory.
In a fourth aspect, embodiments of the present invention further provide a computer readable storage medium storing a program or instructions that cause a computer to perform any one of the threshold configuration and constellation failure determination methods described above.
The embodiment of the invention has the advantages that: according to the invention, all detection threshold values set by a data set divided according to fault types in a log are obtained, and weighted average is carried out on all detection threshold values to generate a corresponding type fault history detection threshold baseline; predicting a short-term fluctuation trend of a detection threshold baseline according to a data set divided according to fault types in a log; predicting a mid-term fluctuation trend of a detection threshold baseline according to a data set divided according to fault types in the log and the historical detection threshold baseline; predicting a long-term fluctuation trend of a detection threshold baseline according to a data set divided according to fault types in a log and the historical detection threshold baseline; determining whether the short-term threshold baseline is changed according to the short-term fluctuation trend of the detection threshold baseline and the short-term warning quantity trend change; determining whether the medium-term threshold baseline is changed according to medium-term fluctuation trend and medium-term alarm quantity trend changes of the detection threshold baseline; determining whether the long-term threshold baseline is changed according to the long-term fluctuation trend of the detection threshold baseline and the change of the long-term alarm quantity trend; and judging whether the constellation fails according to the changed threshold baseline. The invention can identify single star fault and multi-star fault through dynamically configuring the threshold baseline.
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In order to more clearly illustrate the technical solutions of the embodiments or the conventional techniques of the present invention, the drawings required for the descriptions of the embodiments or the conventional techniques will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to the drawings without inventive effort for those skilled in the art.
Fig. 1 is a schematic diagram of a threshold configuration and constellation fault judging method according to an embodiment of the present invention;
fig. 2 is a schematic diagram of a threshold configuration and constellation fault determining apparatus according to an embodiment of the present invention;
fig. 3 is a schematic block diagram of an electronic device provided by an embodiment of the present disclosure.
Detailed Description
In order that the above objects, features and advantages of the invention will be readily understood, a more particular description of the invention will be rendered by reference to the appended drawings. In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention. The invention may be embodied in many other forms than described herein without departing from the spirit or essential characteristics thereof and, therefore, the invention is not limited by the specific embodiments disclosed herein.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "and/or" as used herein includes any and all combinations of one or more of the associated listed items.
Fig. 1 is a schematic diagram of a threshold configuration and constellation failure determination method according to an embodiment of the present invention.
In a first aspect, an embodiment of the present invention provides a method for threshold configuration and constellation failure determination, and in combination with fig. 1, the method includes steps S101 to S108:
s101: acquiring all detection threshold values set by a data set divided according to fault types in a log, and generating a corresponding type fault history detection threshold baseline after carrying out weighted average on all detection threshold values;
s102: and predicting the short-term fluctuation trend of the detection threshold baseline according to the data set divided according to the fault types in the log.
Specifically, in the embodiment of the present invention, a method for predicting a short-term fluctuation trend of a detection threshold baseline according to a data set divided according to fault types in a log is described in detail below.
S103: and predicting the medium-term fluctuation trend of the detection threshold base line according to the data set divided according to the fault types in the log and the historical detection threshold base line.
Specifically, in the embodiment of the present invention, a method for predicting a mid-term fluctuation trend of a detection threshold baseline according to a data set divided by fault types in a log and a historical detection threshold baseline is described in detail below.
S104: and predicting the long-term fluctuation trend of the detection threshold base line according to the data set divided according to the fault types in the log and the historical detection threshold base line.
Specifically, in the embodiment of the present invention, a method for predicting a long-term fluctuation trend of a detection threshold baseline according to a data set divided according to fault types in a log and the historical detection threshold baseline is described in detail below.
S105: and determining whether the short-term threshold baseline is changed or not according to the short-term fluctuation trend of the detection threshold baseline and the short-term warning quantity trend change.
Specifically, in the embodiment of the present invention, a method for determining whether the short-term threshold baseline is changed according to the short-term fluctuation trend of the detection threshold baseline and the short-term alarm quantity trend change is described in detail below.
S106: and determining whether the medium-term threshold baseline is changed according to the medium-term fluctuation trend and the medium-term alarm quantity trend change of the detection threshold baseline.
Specifically, in the embodiment of the present invention, a method for determining whether the middle threshold baseline is changed according to the middle fluctuation trend and the middle alarm number trend change of the detection threshold baseline is described in detail below.
S107: and determining whether the long-term threshold baseline is changed according to the long-term fluctuation trend of the detection threshold baseline and the change of the long-term alarm quantity trend.
Specifically, in the embodiment of the present invention, a method for determining whether the long-term threshold baseline is changed according to the long-term fluctuation trend of the detection threshold baseline and the change of the long-term alarm quantity trend is described in detail below.
S108: and judging whether the constellation fails according to the changed threshold baseline.
Specifically, in the embodiment of the invention, the distances between all the subset solutions and the global solution are smaller than or equal to the threshold, namely the changed threshold baseline, and the current visible satellite is considered to have no fault; otherwise, the faulty satellite is considered to be present. It can also be seen from this that not only single star faults can be identified, but also multi-star faults.
Further, in the above method for configuring threshold and judging constellation fault, predicting short-term fluctuation trend of the detection threshold baseline according to the data set divided according to fault type in the log includes:
adopting a data set divided according to fault types in a Markov chain prediction model training log, and predicting the probability of abnormality of a detection threshold at the time T+1 through the time T data;
the Markov chain prediction model formula is X (k+1) =X (k) ×P
Wherein X (k) represents a state vector of the trend analysis and prediction object at time t=k, P represents a one-step transition probability matrix, and X (k+1) represents a state vector of the trend analysis and prediction object at time t=k+1.
Specifically, exemplary, it is calculated by the following rectangular data analysis
Upper period detection threshold anomaly probability [ 0.3, 0.7 ]
Normal probability of abnormal transition of detection threshold in current period [ 0.6, 0.4 ]
Normal transition abnormal probability of detection threshold in current period [ 0.3, 0.7 ]
Abnormal probability of the detection threshold of the next period:
0.3x0.6+0.3x0.7＝0.39
the lower period detection threshold normal probability:
0.3x0.4+7x0.7＝0.61
finally, the detection threshold probability of the next period is abnormal 39%, normal 61% [ 0.39.0.61 ].
Further, in the above method for configuring a threshold and determining a constellation fault, predicting a mid-term fluctuation trend of a detection threshold baseline according to a data set divided by fault type in a log and the historical detection threshold baseline includes:
obtaining a value with change of the latest N detection thresholds set by a data set divided according to fault types in a log and adding the currently set value to total N+1 times as denominators; comparing the detected molecular weight with a historical detection threshold baseline one by one, judging that the detected molecular weight is abnormal when the detected molecular weight is higher than the baseline, and otherwise, judging that the detected molecular weight is normal;
and determining the probability of occurrence of abnormality in the middle of the baseline of the detection threshold according to the denominator and the numerator.
Specifically, the exemplary N is 9, and a total of ten values obtained by adding the value of the last 9 detection threshold changes set by the data set divided according to the fault type in the log to the currently set value is taken as a denominator. And comparing the detection threshold with the historical detection threshold base line one by one, judging that the detection threshold base line is abnormal if the detection threshold base line is higher than the base line, otherwise, taking the detection threshold base line as a molecule, and determining the probability of occurrence of the abnormality in the middle of the detection threshold base line according to denominator and the molecule.
Further, in the above method for configuring a threshold and determining a constellation fault, predicting a long-term fluctuation trend of a detection threshold baseline according to a data set divided by fault type in a log and the historical detection threshold baseline includes:
obtaining a value with change of a latest M detection threshold set by a data set divided according to fault types in a log and adding a currently set value to total M+1 times as a denominator; comparing the detected molecular weight with a historical detection threshold baseline one by one, judging that the detected molecular weight is abnormal when the detected molecular weight is higher than the baseline, and otherwise, judging that the detected molecular weight is normal;
and determining the probability of long-term abnormality of the detection threshold baseline according to the denominator and the numerator.
Specifically, in the embodiment of the present invention, M is greater than N, and the exemplary M takes 99, and the sum of the value of the last 99 detection thresholds set by the data set divided according to the fault type in the obtained log and the value currently set is added to one hundred times as the denominator. And comparing the detection threshold base lines with the historical detection threshold base lines one by one, judging that the detection threshold base lines are abnormal when the detection threshold base lines are higher than the base lines, otherwise, taking the detection threshold base lines as molecules, and determining the probability of long-term abnormality of the detection threshold base lines according to denominators and the molecules.
Further, in the above method for configuring a threshold and determining a constellation fault, determining whether the short-term threshold baseline is changed according to the short-term fluctuation trend of the detection threshold baseline and the short-term alarm quantity trend change includes:
and carrying out linkage analysis on the short-term abnormal probability of the detection threshold baseline and the short-term warning quantity trend change to determine whether the short-term threshold baseline is changed.
Specifically, in the embodiment of the invention, if the short-term abnormal probability of the detection threshold baseline is 50%, the short-term warning number trend is changed to 10 to 15, and the warning trend is increased, the short-term threshold baseline is determined to be unchanged.
Further, in the above method for configuring a threshold and determining a constellation fault, determining whether the medium-term threshold baseline is changed according to the medium-term fluctuation trend and the medium-term alarm quantity trend change of the detection threshold baseline includes:
and carrying out linkage analysis on the middle abnormal probability of the detection threshold baseline and the middle alarm quantity trend change to determine whether the middle threshold baseline is changed.
Specifically, in the embodiment of the invention, if the middle abnormal probability of the baseline of the detection threshold is 40%, the trend of the number of middle alarms is changed to 25 to 15, and the alarm trend is reduced, the baseline of the middle threshold is adjusted downwards.
Further, in the above method for configuring a threshold and determining a constellation fault, determining whether the long-term threshold baseline is changed according to the long-term fluctuation trend of the detection threshold baseline and the change of the long-term alarm quantity trend includes:
and carrying out linkage analysis on the long-term abnormal probability of the detection threshold baseline and the trend change of the number of long-term alarms to determine whether the long-term threshold baseline is changed.
Specifically, in the embodiment of the invention, if the long-term abnormal probability of the detection threshold baseline is 30%, the trend of the long-term alarm quantity is changed to 35 to 55, and the alarm trend is increased, the long-term threshold baseline is adjusted upwards.
Fig. 2 is a schematic diagram of a threshold configuration and constellation fault determining apparatus according to an embodiment of the present invention.
In a second aspect, an embodiment of the present invention further provides a threshold configuration and constellation fault determining apparatus, with reference to fig. 2, including:
acquisition module 201 and generation module 202: and the method is used for acquiring all detection threshold values set by the data set divided according to the fault type in the log, and generating a corresponding type fault history detection threshold baseline after carrying out weighted average on all detection threshold values.
Specifically, in the embodiment of the present invention, the obtaining module 201 is configured to obtain all the detection threshold values set by the data set divided by the fault type in the log, and the generating module 202 performs weighted average on the all the detection threshold values to generate a corresponding type of fault history detection threshold baseline.
The first prediction module 203: and the method is used for predicting the short-term fluctuation trend of the detection threshold baseline according to the data set divided according to the fault types in the log.
Specifically, in the embodiment of the present invention, the method of the first prediction module 203 for predicting the short-term fluctuation trend of the detection threshold baseline according to the data set divided by the fault type in the log is described in detail above.
The second prediction module 204: and the method is used for predicting the mid-term fluctuation trend of the detection threshold base line according to the data set divided according to the fault types in the log and the historical detection threshold base line.
Specifically, in the embodiment of the present invention, the method of the second prediction module 204 for predicting the mid-term fluctuation trend of the detection threshold baseline according to the data set and the historical detection threshold baseline divided by the fault type in the log is described in detail above.
The third prediction module 205: the method is used for predicting the long-term fluctuation trend of the detection threshold base line according to the data set divided according to the fault types in the log and the historical detection threshold base line.
Specifically, in the embodiment of the present invention, the method for predicting the long-term fluctuation trend of the detection threshold baseline by the third prediction module 205 according to the data set and the historical detection threshold baseline divided by the fault type in the log is described in detail above.
The first determination module 206: and the method is used for determining whether the short-term threshold baseline is changed or not according to the short-term fluctuation trend of the detection threshold baseline and the short-term warning quantity trend change.
Specifically, in the embodiment of the present invention, the method for determining whether the short-term threshold baseline is changed by the first determining module 206 according to the short-term fluctuation trend of the detection threshold baseline and the short-term alarm quantity trend change is described in detail above.
The second determination module 207: and the medium-term threshold baseline is used for determining whether the medium-term threshold baseline is changed or not according to the medium-term fluctuation trend and the medium-term alarm quantity trend change of the detection threshold baseline.
Specifically, in the embodiment of the present invention, the method for determining whether the mid-term threshold baseline is changed according to the mid-term fluctuation trend of the detection threshold baseline and the mid-term alarm number trend change by the second determining module 207 is described in detail above.
The third determination module 208: and the method is used for determining whether the long-term threshold baseline is changed or not according to the long-term fluctuation trend of the detection threshold baseline and the change of the long-term alarm quantity trend.
Specifically, in the embodiment of the present invention, the method for determining whether the long-term threshold baseline is changed according to the long-term fluctuation trend of the detection threshold baseline and the long-term alarm quantity trend change by the third determining module 208 is described in detail above.
The judging module 209: and judging whether the constellation is faulty or not according to the changed threshold baseline.
Specifically, the judging module in the embodiment of the invention judges whether the current visible satellite has faults or not according to the distances between all the subset solutions and the global solution and the changed threshold base line, and can recognize that the single satellite faults can be recognized, and the multi-satellite faults can be recognized.
In a third aspect, an embodiment of the present invention further provides an electronic device, including: a processor and a memory;
the processor is used for executing any one of the threshold configuration and constellation fault judgment methods by calling the program or the instruction stored in the memory.
In a fourth aspect, embodiments of the present invention further provide a computer readable storage medium storing a program or instructions that cause a computer to perform any one of the threshold configuration and constellation failure determination methods described above.
Fig. 3 is a schematic block diagram of an electronic device provided by an embodiment of the present disclosure.
As shown in fig. 3, the electronic device includes: at least one processor 301, at least one memory 302, and at least one communication interface 303. The various components in the electronic device are coupled together by a bus system 304. A communication interface 303 for information transfer with an external device. It is understood that bus system 304 is used to enable connected communications between these components. The bus system 304 includes a power bus, a control bus, and a status signal bus in addition to the data bus. The various buses are labeled in fig. 3 as bus system 304 for clarity of illustration.
It is to be understood that the memory 302 in this embodiment may be either volatile memory or nonvolatile memory, or may include both volatile and nonvolatile memory.
In some implementations, the memory 302 stores the following elements, executable units or data structures, or a subset thereof, or an extended set thereof: an operating system and application programs.
The operating system includes various system programs, such as a framework layer, a core library layer, a driving layer, and the like, and is used for realizing various basic services and processing hardware-based tasks. Applications, including various applications such as Media Player (Media Player), browser (Browser), etc., are used to implement various application services. The program for implementing any one of the threshold configuration and constellation fault judgment methods provided by the embodiments of the present invention may be included in the application program.
In the embodiment of the present invention, the processor 301 is configured to execute the steps of the embodiments of the threshold configuration and constellation fault determination method provided in the embodiment of the present invention by calling a program or an instruction stored in the memory 302, specifically, a program or an instruction stored in an application program.
In a first aspect, an embodiment of the present invention provides a method for threshold configuration and constellation failure determination, including:
acquiring all detection threshold values set by a data set divided according to fault types in a log, and generating a corresponding type fault history detection threshold baseline after carrying out weighted average on all detection threshold values;
predicting a short-term fluctuation trend of a detection threshold baseline according to a data set divided according to fault types in a log;
predicting a mid-term fluctuation trend of a detection threshold baseline according to a data set divided according to fault types in the log and the historical detection threshold baseline;
predicting a long-term fluctuation trend of a detection threshold baseline according to a data set divided according to fault types in a log and the historical detection threshold baseline;
determining whether the short-term threshold baseline is changed according to the short-term fluctuation trend of the detection threshold baseline and the short-term warning quantity trend change;
determining whether the medium-term threshold baseline is changed according to medium-term fluctuation trend and medium-term alarm quantity trend changes of the detection threshold baseline;
determining whether the long-term threshold baseline is changed according to the long-term fluctuation trend of the detection threshold baseline and the change of the long-term alarm quantity trend;
and judging whether the constellation fails according to the changed threshold baseline.
Any one of the threshold configuration and constellation fault judging methods provided in the embodiments of the present invention may be applied to the processor 301, or may be implemented by the processor 301. The processor 301 may be an integrated circuit chip with signal capabilities. In implementation, the steps of the above method may be performed by integrated logic circuitry of hardware in the processor 301 or instructions in the form of software. The processor 301 may be a general purpose processor, a digital signal processor (Digital Signal Processor, DSP), an application specific integrated circuit (Application Specific Integrated Circuit, ASIC), an off-the-shelf programmable gate array (Field Programmable Gate Array, FPGA) or other programmable logic device, discrete gate or transistor logic device, discrete hardware components. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The steps of any one of the threshold configuration and constellation fault judging methods provided in the embodiments of the present invention may be directly embodied in the execution of a hardware decoding processor, or in the execution of a combination of hardware and software units in the decoding processor. The software elements may be located in a random access memory, flash memory, read-only memory, programmable read-only memory or electrically erasable programmable memory, registers, etc. as well known in the art. The storage medium is located in a memory 302, and the processor 301 reads the information in the memory 302, and combines the hardware to complete the steps of a threshold configuration and constellation fault determination method.
Those skilled in the art will appreciate that while some embodiments described herein include some features but not others included in other embodiments, combinations of features of different embodiments are meant to be within the scope of the invention and form different embodiments.
Those skilled in the art will appreciate that the descriptions of the various embodiments are each focused on, and that portions of one embodiment that are not described in detail may be referred to as related descriptions of other embodiments.
The present invention is not limited to the above embodiments, and various equivalent modifications and substitutions can be easily made by those skilled in the art within the technical scope of the present invention, and these modifications and substitutions are intended to be included in the scope of the present invention. Therefore, the protection scope of the invention is subject to the protection scope of the claims.
Claims (10)
1. A threshold configuration and constellation fault judging method is characterized by comprising the following steps:
acquiring all detection threshold values set by a data set divided according to fault types in a log, and generating a corresponding type fault history detection threshold baseline after carrying out weighted average on all detection threshold values;
predicting a short-term fluctuation trend of a detection threshold baseline according to a data set divided according to fault types in the log;
predicting a mid-term fluctuation trend of a detection threshold baseline according to a data set divided according to fault types in the log and the historical detection threshold baseline;
predicting a long-term fluctuation trend of a detection threshold baseline according to a data set divided according to fault types in the log and the historical detection threshold baseline;
determining whether the short-term threshold baseline is changed according to the short-term fluctuation trend of the detection threshold baseline and the short-term warning quantity trend change;
determining whether the medium-term threshold baseline is changed according to medium-term fluctuation trend and medium-term alarm quantity trend changes of the detection threshold baseline;
determining whether the long-term threshold baseline is changed according to the long-term fluctuation trend of the detection threshold baseline and the change of the long-term alarm quantity trend;
and judging whether the constellation fails according to the changed threshold baseline.
2. The method for configuring threshold and determining constellation faults according to claim 1, wherein predicting a short-term fluctuation trend of a detection threshold based on a data set divided by fault type in the log comprises:
adopting a data set divided according to fault types in a Markov chain prediction model training log, and predicting the probability of abnormality of a detection threshold at the time T+1 through the time T data;
the Markov chain prediction model formula is X (k+1) =X (k) ×P
Wherein X (k) represents a state vector of the trend analysis and prediction object at time t=k, P represents a one-step transition probability matrix, and X (k+1) represents a state vector of the trend analysis and prediction object at time t=k+1.
3. The method for configuring and determining a constellation fault according to claim 1, wherein predicting a mid-term fluctuation trend of a detection threshold baseline according to a data set divided by fault type in the log and the historical detection threshold baseline comprises:
obtaining a value with change of the latest N detection thresholds set by a data set divided according to fault types in a log and adding the currently set value to total N+1 times as denominators; comparing the detected molecular weight with the historical detection threshold baseline one by one, judging that the detected molecular weight is abnormal if the detected molecular weight is higher than the baseline, and otherwise, judging that the detected molecular weight is normal;
and determining the probability of occurrence of abnormality in the middle period of the baseline of the detection threshold according to the denominator and the numerator.
4. The method for configuring and determining a constellation fault according to claim 1, wherein predicting a long-term fluctuation trend of a detection threshold baseline according to a data set divided by fault type in the log and the historical detection threshold baseline comprises:
obtaining a value with change of a latest M detection threshold set by a data set divided according to fault types in a log and adding a currently set value to total M+1 times as a denominator; comparing the detected molecular weight with the historical detection threshold baseline one by one, judging that the detected molecular weight is abnormal if the detected molecular weight is higher than the baseline, and otherwise, judging that the detected molecular weight is normal;
and determining the probability of long-term abnormality of the detection threshold baseline according to the denominator and the numerator.
5. The method for configuring threshold and determining constellation failure according to claim 1, wherein determining whether the short-term threshold baseline is changed according to the short-term fluctuation trend of the detection threshold baseline and the short-term alarm quantity trend change comprises:
and carrying out linkage analysis on the short-term abnormal probability of the detection threshold baseline and the short-term warning quantity trend change to determine whether the short-term threshold baseline is changed.
6. The method for configuring a threshold and determining a constellation failure according to claim 1, wherein determining whether the mid-term threshold baseline is changed according to the mid-term fluctuation trend and the mid-term alarm number trend change of the detection threshold baseline comprises:
and carrying out linkage analysis on the middle abnormal probability of the detection threshold baseline and the middle alarm quantity trend change to determine whether the middle threshold baseline is changed.
7. The method for configuring and determining a constellation failure as defined in claim 1, wherein determining whether the long-term threshold baseline is changed according to the long-term fluctuation trend of the detection threshold baseline and the change of the long-term alarm quantity trend comprises:
and carrying out linkage analysis on the long-term abnormal probability of the detection threshold baseline and the trend change of the number of long-term alarms to determine whether the long-term threshold baseline is changed.
8. A threshold configuration and constellation failure determination apparatus, comprising:
the device comprises an acquisition module and a generation module: the method comprises the steps of obtaining all detection threshold values set by a data set divided according to fault types in a log, and generating a corresponding type fault history detection threshold baseline after carrying out weighted average on all detection threshold values;
a first prediction module: the method comprises the steps of predicting a detection threshold baseline short-term fluctuation trend according to a data set divided according to fault types in a log;
a second prediction module: the method comprises the steps of predicting a mid-term fluctuation trend of a detection threshold baseline according to a data set divided according to fault types in a log and the historical detection threshold baseline;
a third prediction module: the method comprises the steps of predicting a long-term fluctuation trend of a detection threshold baseline according to a data set divided according to fault types in a log and the historical detection threshold baseline;
a first determination module: the method comprises the steps of determining whether a short-term threshold baseline is changed according to short-term fluctuation trend of a detection threshold baseline and short-term alarm quantity trend change;
a second determination module: the medium-term threshold baseline is used for determining whether the medium-term threshold baseline is changed or not according to the medium-term fluctuation trend and the medium-term alarm quantity trend change of the detection threshold baseline;
and a third determination module: the method comprises the steps of determining whether a long-term threshold baseline is changed according to a long-term fluctuation trend of a detection threshold baseline and a long-term alarm quantity trend change;
and a judging module: and judging whether the constellation is faulty or not according to the changed threshold baseline.
9. An electronic device, comprising: a processor and a memory;
the processor is configured to execute a threshold configuration and constellation failure determination method according to any one of claims 1 to 7 by calling a program or instructions stored in the memory.
10. A computer-readable storage medium storing a program or instructions that cause a computer to perform a threshold configuration and constellation failure determination method according to any of claims 1 to 7.
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