A channel detection method for DOS system

Abstract

The present invention relates to the field of channel detection technology, and discloses a channel detection method for a DOS system, including the steps of data collection, data caching, data feature analysis, channel status determination, and determination result output. The present invention can collect multi-faceted channel data and deeply analyze its features, comprehensively reflect the channel status, accurately determine its normal or abnormal state, and reduce the risk of misjudgment; by establishing a data cache area to store data in chronological order, the data change trend within a specified period can be analyzed; by calculating the average value and variance of data flow, the channel characteristics can be understood from multiple angles, providing strong support for accurate determination. The feature quantization method is adopted, and the comprehensive determination value is calculated in combination with the preset weight coefficient to determine the channel state, so that the determination process is more scientific, objective and operable, and can be comprehensively considered according to the importance of each feature, effectively improving the determination reliability, and ensuring the stability and reliability of the data transmission of the DOS system.

Description

Technical Field

[0001] The present invention relates to the technical field of channel detection, and in particular to a channel detection method for a DOS system. Background Art

[0002] In the operation of DOS system, the stability and reliability of the channel plays a vital role in the accurate transmission of data. With the continuous expansion of DOS system application scenarios and the increasing requirements for data transmission, traditional channel detection methods have gradually exposed many shortcomings.

[0003] Traditional channel detection often only focuses on a single or a few channel parameters. For example, it may simply monitor signal strength while ignoring other key factors such as data flow, data error rate, and noise level. This one-sided detection method cannot fully and accurately reflect the true status of the channel. Judging the channel only based on signal strength may fail to detect potential problems in the channel in a timely manner when the signal strength seems normal but the data error rate is too high or the data flow fluctuates abnormally, resulting in data transmission errors, delays, or even interruptions, which seriously affects the overall performance of the DOS system and the reliability of data transmission.

[0004] Moreover, there is a lack of systematic, multi-dimensional data collection, caching and analysis mechanisms. In the past, there was no complete data cache area to orderly store channel-related data for multiple time periods, making it difficult to deeply analyze the changing trends of data features from a time series perspective, and it is impossible to effectively use historical data to assist in the determination of channel status. In addition, during the determination process, there is a lack of a scientific and reasonable quantitative evaluation system, and it is impossible to comprehensively consider the importance of the impact of each feature on the channel, which makes the channel status determination results highly subjective and uncertain, and it is difficult to meet the DOS system's requirements for high-precision and high-reliability channel detection.

[0005] To sum up, in order to overcome the defects of traditional channel detection methods and ensure the stability and efficiency of data transmission in DOS systems, there is an urgent need for a new channel detection method that can comprehensively collect multi-faceted channel data, scientifically analyze data characteristics and accurately determine the channel status. This is the important background and research and development driving force for the present invention. Summary of the invention

[0006] The purpose of the present invention is to provide a channel detection method for a DOS system, which solves the technical problems raised in the background technology.

[0007] The purpose of the present invention can be achieved through the following technical solutions:

[0008] A channel detection method for a DOS system comprises the following steps:

[0009] Step 1: Data collection:

[0010] First, determine the collection time interval T;

[0011] Then, during the time interval T, the data flow, data error rate signal strength and noise level related to the channel are collected and recorded as F, E, S and U respectively;

[0012] Step 2: Data caching:

[0013] Establish a data buffer area to store the collected data in chronological order;

[0014] Step 3: Data feature analysis:

[0015] Perform feature analysis on all data in the data buffer area and determine the status of data flow features, data error rate features, signal strength features, and noise level features within a specified period;

[0016] Step 4: Channel status determination:

[0017] According to the analysis result of the data feature analysis in step 3, the channel state is comprehensively determined, wherein the channel state includes a normal state and an abnormal state of the channel;

[0018] Step 5: Output of determination result:

[0019] The data flow characteristics, data error rate characteristics, signal strength characteristics, noise level characteristics within a specified period and the channel status determination results are displayed to relevant personnel.

[0020] As a further solution of the present invention: the storage method of the data buffer area is as follows:

[0021] In a specified period, the data flow, data error rate, signal strength and noise level collected at each time point are recorded as F i 、E i , S i and U i , where i=1, 2, ... n, and n represents the number of time points in the specified period.

[0022] As a further solution of the present invention: the characteristic analysis method is as follows:

[0023] StepA1, Data traffic characteristics analysis:

[0024] StepA1.1. Extract all data traffic Fi in the data buffer area;

[0025] StepA1.2, through:

[0026] ,

[0027] Calculate the average value PJ of all data traffic within the specified period F ;

[0028] StepA1.3, through:

[0029] ,

[0030] Calculate the variance FC of all data traffic within a specified period F ;

[0031] StepA1.4, the variance of data flow FC F and the preset flow variance threshold FCY F For comparison:

[0032] If FC F ≤FCY F , it means that the data traffic is relatively stable, that is, the data traffic is in a normal state;

[0033] If FC F >FCY F , it means that the data traffic fluctuates greatly, that is, the data traffic is in an abnormal state;

[0034] StepA2, data error rate characteristics analysis:

[0035] StepA2.1. Extract all data error rates E in the data buffer area i ;

[0036] StepA1.2, through:

[0037] ,

[0038] Calculate the average PJ of all data error rates within a specified period E ;

[0039] StepA2.2, average value PJ of data error rate E Compared with the preset data error rate threshold PJY E For comparison:

[0040] If PJ E >PJY E , it means that the channel has a high error rate, which means that there is interference or hardware failure in the channel, that is, the data error rate is in an abnormal state;

[0041] If PJ E ≤PJY E , it means that the error rate of the channel is within the permitted range, that is, the data error rate is in a normal state;

[0042] StepA3, Signal strength characteristics analysis:

[0043] StepA3.1. Extract all signal strengths S in the data buffer. i ;

[0044] StepA3.2, through:

[0045] ,

[0046] Calculate the average value PJ of all signal strengths within the specified period S ;

[0047] StepA3.3, through:

[0048] ,

[0049] Calculate the rate of change of signal strength BH within a specified period S ;

[0050] StepA3.4, the average value PJ of all signal strengths within the specified period S With the preset signal strength threshold PJY S For comparison, the rate of change of signal intensity BH S The signal strength change rate threshold BHY S For comparison:

[0051] If PJ S >PJY S and BH S >BHY S If at least one of the following holds true, it means that the signal strength is in an abnormal state, which in turn affects the channel quality;

[0052] If PJ S ≤PJY S and BH S ≤BHY S If all of them are true, it means that the signal strength is in normal state;

[0053] StepA4, noise level characteristics analysis:

[0054] StepA4.1. Extract all noise level values ​​U in the data buffer. i ;

[0055] StepA4.2, through:

[0056] ,

[0057] Calculate the root mean square value JF of all noise levels within a specified periodU ;

[0058] StepA4.3, at all noise level values ​​U i The maximum noise level value is extracted and recorded as the noise peak value U peak ;

[0059] Then through:

[0060] ,

[0061] Calculate the peak factor FZ of the noise level within a specified period U ;

[0062] StepA4.4. Peak factor FZ of noise level U With the preset noise peak factor threshold FZY U For comparison:

[0063] If FZ U >FZY U , it means that the noise level is in an abnormal state, which in turn affects the channel quality;

[0064] If FZ U ≤FZY U , it means the noise level is normal.

[0065] As a further solution of the present invention: the comprehensive determination method of step 4 is as follows:

[0066] StepB1, weight extraction:

[0067] Extract the preset weight coefficients corresponding to the data flow characteristics, data error rate characteristics, signal strength characteristics, and noise level characteristics, and record them as W F , W E , W S and W U ;

[0068] Among them, W F +W E +W S +W U =1, the corresponding weight coefficient is preset according to experience or the importance of each feature on the channel;

[0069] Step B2, feature quantification:

[0070] Step B2.1, for data traffic characteristics:

[0071] When the data flow is normal, L F =1 as the quantitative value of data flow characteristics;

[0072] When the data traffic is in an abnormal state, L F =-1 as the quantitative value of data flow characteristics;

[0073] Step B2.2, for data error rate characteristics:

[0074] When the data error rate is normal, L E =1 as the quantized value of the data error rate feature;

[0075] When the data error rate is abnormal, L E =-1 as the quantitative value of the data error rate feature;

[0076] Step B2.3, for signal strength characteristics:

[0077] When the signal strength is normal, L S =1 as the quantized value of the signal strength feature;

[0078] When the signal strength is abnormal, L S =-1 as the quantized value of the signal strength feature;

[0079] Step B2.4, for noise level characteristics:

[0080] When the noise level is normal, L U =1 as the quantized value of the noise level feature;

[0081] When the noise level is abnormal, L U = -1 as the quantized value of the noise level feature;

[0082] Step B3, calculation of comprehensive judgment value:

[0083] pass:

[0084] ,

[0085] Calculate the comprehensive judgment value Q of the channel;

[0086] Step B4, channel status determination:

[0087] According to the comprehensive judgment value Q of the channel, the channel state is determined;

[0088] When Q ≥ 0, it means that the channel is in a normal state;

[0089] When Q<0, it indicates that the channel is in an abnormal state.

[0090] Beneficial effects of the present invention:

[0091] Comprehensive and accurate detection: By collecting channel-related data such as data flow, data error rate, signal strength, and noise level, and conducting in-depth analysis of the characteristics of these data, it can comprehensively reflect the actual status of the channel, thereby more accurately determining the channel status, and effectively identifying both normal and abnormal states, reducing the risk of misjudgment.

[0092] Advantages of data caching and analysis: Establishing a data cache area and storing data in chronological order facilitates overall feature analysis of data within a specified period, and can obtain the changing trends and patterns of data over a period of time. For example, by calculating the mean and variance of data traffic to determine its stability, and calculating the mean and change rate of signal strength, etc., it can deeply understand channel characteristics from different angles and provide a strong basis for accurate judgment.

[0093] The quantification judgment is scientific and reasonable: the characteristics of data flow, data error rate, signal strength and noise level are quantified to 1 or -1 respectively by using the feature quantification method, and then the comprehensive judgment value is calculated in combination with the preset weight coefficient to determine the channel status. This quantification process makes the judgment process more scientific, objective and operational. It can comprehensively consider the importance of the impact of different characteristics on the channel, improve the reliability and effectiveness of the channel status judgment, help to timely discover the problems in the channel and take corresponding measures to ensure the stability and reliability of data transmission in the DOS system. BRIEF DESCRIPTION OF THE DRAWINGS

[0094] The present invention will be further described below in conjunction with the accompanying drawings.

[0095] Figure 1 The present invention is a schematic flow chart of a channel detection method for a DOS system.

[0096] Figure 2 The present invention is a schematic flow chart of data feature analysis in a channel detection method for a DOS system.

[0097] Figure 3 It is a schematic flow chart of channel status determination in a channel detection method for a DOS system according to the present invention. DETAILED DESCRIPTION

[0098] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0099] Embodiment 1

[0100] See also Figures 1 to 3 As shown, the present invention is a channel detection method for a DOS system, comprising the following steps:

[0101] Step 1: Data collection:

[0102] First, determine the collection time interval T;

[0103] Then, during the time interval T, the data flow, data error rate signal strength and noise level related to the channel are collected and recorded as F, E, S and U respectively;

[0104] In this embodiment, the unit of data flow is byte / second, the data error rate is expressed as the percentage of the number of erroneous data bytes to the total number of transmitted data bytes, and the signal strength is expressed in milliwatts;

[0105] In this embodiment, the time interval T is set to 5 milliseconds, and this time interval can be adjusted according to actual needs;

[0106] Step 2: Data caching:

[0107] Establish a data buffer area to store the collected data in chronological order;

[0108] The storage method is as follows:

[0109] In a specified period, the data flow, data error rate, signal strength and noise level collected at each time point are recorded as F i 、E i , S i and U i , where i=1, 2, ... n, and n represents the number of time points in the specified period;

[0110] Step 3: Data feature analysis:

[0111] StepA1, Data traffic characteristics analysis:

[0112] StepA1.1. Extract all data traffic Fi in the data buffer area;

[0113] StepA1.2, through:

[0114] ,

[0115] Calculate the average value PJ of all data traffic within the specified period F ;

[0116] StepA1.3, through:

[0117] ,

[0118] Calculate the variance FC of all data traffic within a specified period F ;

[0119] StepA1.4, the variance of data flow FC F and the preset flow variance threshold FCY F For comparison:

[0120] If FC F ≤FCY F , it means that the data traffic is relatively stable, that is, the data traffic is in a normal state;

[0121] If FC F >FCY F , it means that the data traffic fluctuates greatly, that is, the data traffic is in an abnormal state;

[0122] StepA2, data error rate characteristics analysis:

[0123] StepA2.1. Extract all data error rates E in the data buffer area i ;

[0124] StepA1.2, through:

[0125] ,

[0126] Calculate the average PJ of all data error rates within a specified period E ;

[0127] StepA2.2, average value PJ of data error rate E Compared with the preset data error rate threshold PJY E For comparison:

[0128] If PJ E >PJY E , it means that the channel has a high error rate, which means that there is interference or hardware failure in the channel, that is, the data error rate is in an abnormal state;

[0129] If PJ E ≤PJY E , it means that the error rate of the channel is within the permitted range, that is, the data error rate is in a normal state;

[0130] StepA3, Signal strength characteristics analysis:

[0131] StepA3.1. Extract all signal strengths S in the data buffer. i ;

[0132] StepA3.2, through:

[0133] ,

[0134] Calculate the average value PJ of all signal strengths within the specified period S ;

[0135] StepA3.3, through:

[0136] ,

[0137] Calculate the rate of change of signal strength BH within a specified period S ;

[0138] StepA3.4, the average value PJ of all signal strengths within the specified period S With the preset signal strength threshold PJY S For comparison, the rate of change of signal intensity BH S The signal strength change rate threshold BHY S For comparison:

[0139] If PJ S >PJY S and BH S >BHY S If at least one of the following holds true, it means that the signal strength is in an abnormal state, which in turn affects the channel quality;

[0140] If PJ S ≤PJY S and BH S ≤BHY S If all of them are true, it means that the signal strength is in normal state;

[0141] StepA4, noise level characteristics analysis:

[0142] StepA4.1. Extract all noise level values ​​U in the data buffer. i ;

[0143] StepA4.2, through:

[0144] ,

[0145] Calculate the root mean square value JF of all noise levels within a specified period U ;

[0146] StepA4.3, at all noise level values ​​U i The maximum noise level value is extracted and recorded as the noise peak value U peak ;

[0147] Then the peak factor of the noise level in a specified period is calculated by dividing the noise peak by the RMS value of all noise levels;

[0148] That is, through the following formula:

[0149] ,

[0150] Calculate the peak factor FZ of the noise level within a specified period U ;

[0151] StepA4.4. Peak factor FZ of noise level U With the preset noise peak factor threshold FZY U For comparison:

[0152] If FZ U >FZY U , it means that the noise level is in an abnormal state, which in turn affects the channel quality;

[0153] If FZ U ≤FZY U , it means the noise level is in normal state;

[0154] Step 4: Output of determination results:

[0155] Display the status of data flow characteristics, data error rate characteristics, signal strength characteristics, and noise level characteristics in a specified period to relevant personnel;

[0156] This embodiment can detect the DOS system channel from multiple dimensions and have a more comprehensive understanding of the channel status by collecting various data related to the channel, including data flow, data error rate, signal strength, and noise level. A data buffer is established and the collected data is stored in chronological order, which is convenient for subsequent feature analysis of data within a specified period, so that the analysis process can be based on a complete data sequence within a certain time period, thereby improving the accuracy and reliability of the analysis results. Detailed feature analysis is performed for different data types, such as calculating the average value and variance of data flow, the average value of data error rate, the average value and change rate of signal strength, the root mean square value and peak factor of the noise level, etc., and comparing them with respective preset thresholds, so as to accurately determine whether the channel status corresponding to each data feature is normal or abnormal, which is helpful to timely discover possible problems with the channel.

[0157] The results are clearly displayed: the data flow characteristics, data error rate characteristics, signal strength characteristics, and noise level characteristics in a specified period can be displayed to relevant personnel, so that relevant personnel can intuitively understand the conditions of various aspects of the channel, which is convenient for subsequent decision-making and taking corresponding measures.

[0158] Embodiment 2

[0159] As the second embodiment of the present invention, when the present application is specifically implemented, compared with the first embodiment, the technical solution of the present embodiment is different from the first embodiment only in that the present embodiment further includes the step of channel state determination:

[0160] This step comprehensively determines the channel state based on the analysis results of the data feature analysis; wherein the channel state includes the normal state and the abnormal state of the channel;

[0161] First pass:

[0162] ,

[0163] Calculate the comprehensive judgment value Q of the channel;

[0164] Where W F , W E , W S and W U are respectively the preset weight coefficients corresponding to the data flow characteristics, data error rate characteristics, signal strength characteristics, and noise level characteristics, L F , L E , L S and L U They are the quantified values ​​of data flow characteristics, data error rate characteristics, signal strength characteristics, and noise level characteristics respectively;

[0165] Among them, W F +W E +W S +W U =1, the corresponding weight coefficient is preset according to experience or the importance of each feature on the channel;

[0166] The method for determining the corresponding quantitative values ​​of the data flow characteristics, data error rate characteristics, signal strength characteristics, and noise level characteristics is as follows:

[0167] When the data flow is in a normal state, 1 is used as the quantitative value of the data flow characteristic, that is, L F =1;

[0168] When the data traffic is in an abnormal state, -1 is used as the quantization value of the data traffic feature, that is, L F =-1;

[0169] When the data error rate is in a normal state, 1 is used as the quantized value of the data error rate feature, that is, L E =1;

[0170] When the data error rate is in an abnormal state, -1 is used as the quantized value of the data error rate feature, that is, L E =-1;

[0171] When the signal strength is in a normal state, 1 is used as the quantized value of the signal strength feature, that is, L S =1;

[0172] When the signal strength is in an abnormal state, -1 is used as the quantized value of the signal strength feature, that is, L S =-1;

[0173] When the noise level is normal, 1 is used as the quantized value of the noise level feature, that is, L U =1;

[0174] When the noise level is in an abnormal state, -1 is used as the quantized value of the noise level feature, that is, L U =-1;

[0175] Then, the channel status is determined according to the comprehensive judgment value Q of the channel;

[0176] The channel status is determined as follows:

[0177] When Q ≥ 0, it means that the channel is in a normal state;

[0178] When Q < 0, it means that the channel is in an abnormal state;

[0179] In this embodiment, the determination result output step further displays the channel status determination result to relevant personnel.

[0180] This embodiment adds a channel state determination step on the basis of the first embodiment. According to the results of data feature analysis, by setting weight coefficients for different data features and calculating the comprehensive determination value Q, the influence of various data features on the channel state can be comprehensively considered, and it can be more scientifically and comprehensively determined whether the channel as a whole is in a normal state or an abnormal state, thereby avoiding the one-sidedness that may occur when judging the channel state based on only a single data feature. The weight coefficient can be preset according to experience or the importance of the influence of each feature on the channel, and the quantized values ​​of features such as data flow, data error rate, signal strength, and noise level are clearly set according to their states. This method enables the determination process to better adapt to different application scenarios and actual needs, and improves the accuracy and practicality of channel state determination. The determination result output step not only displays the state of each data feature, but also displays the channel state determination result to relevant personnel, so that relevant personnel can have a more comprehensive grasp of the channel situation, including the specific data feature state and the overall channel state, so as to make more reasonable response measures.

[0181] Embodiment 3

[0182] As the third embodiment of the present invention, when the present application is specifically implemented, compared with the first and second embodiments, the technical solution of this embodiment is to combine the solutions of the first and second embodiments mentioned above for implementation.

[0183] This embodiment combines the solutions of embodiment one and embodiment two for implementation, which can not only realize the comprehensive collection, caching and detailed analysis of various aspects of channel data in embodiment one, but also have the advantage of comprehensive determination of channel status in embodiment two, so that in the process of channel detection in the DOS system, the actual situation of the channel can be understood more systematically and accurately, problems existing in the channel can be discovered in time and the overall status of the channel can be accurately determined, providing more powerful support for ensuring the normal operation of the channel.

[0184] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters and thresholds in the formula are set by technicians in this field according to actual conditions.

[0185] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A channel detection method for a DOS system, characterized in that: The following steps are involved: Step 1: first determine the time interval for collection, and then collect the data flow, data error rate, signal strength and noise level related to the channel within the time interval; Step 2: Create a data buffer area to store the collected data in chronological order. The storage method of the data buffer area is as follows: In a specified period, the data flow, data error rate, signal strength and noise level collected at each time point are recorded as F i 、E i , S i and U i , where i=1, 2, ... n, and n represents the number of time points in the specified period; Step 3: Perform feature analysis on the channel-related data stored in the data buffer area, and determine the state of data flow characteristics, data error rate characteristics, signal strength characteristics, and noise level characteristics within a specified period; The data traffic characteristics analysis method is as follows: StepA1.

1. Extract all data traffic F from the data buffer area i ; StepA1.2, calculate the average value of all data traffic within the specified period; StepA1.3, combined with the average value of all data traffic, calculate the variance of all data traffic in the specified period and record it as FC F ; StepA1.4, the variance of data flow FC F and the preset flow variance threshold FCY F For comparison: If FC F ≤FCY F , it means that the data traffic is in normal state; If FC F >FCY F , it means that the data traffic is in an abnormal state; The data error rate characteristic analysis method is as follows: StepA2.

1. Extract all data error rates E in the data buffer area i ; StepA1.2, calculate the average error rate of all data in the specified period and record it as PJ E ; StepA2.2, average value PJ of data error rate E Compared with the preset data error rate threshold PJY E For comparison: If PJ E >PJY E , it means that the channel has a high error rate, which means that there is interference or hardware failure in the channel, that is, the data error rate is in an abnormal state; If PJ E ≤PJY E , it means that the error rate of the channel is within the permitted range, that is, the data error rate is in a normal state; The signal strength characteristics are analyzed as follows: StepA3.

1. Extract all signal strengths S in the data buffer. i ; StepA3.2, calculate the average value of all signal strengths within the specified period and record it as PJ S ; StepA3.3, through: Calculate the rate of change of signal strength BH within a specified period S ; StepA3.4, the average value PJ of all signal strengths within the specified period S With the preset signal strength threshold PJY S For comparison, the rate of change of signal intensity BH S The signal strength change rate threshold BHY S For comparison: If PJ S >PJY S and BH S >BHY S If at least one of the following is true, it means that the signal strength is in an abnormal state; If PJ S ≤PJY S and BH S ≤BHY S If all of them are true, it means that the signal strength is in normal state; The noise level characteristics are analyzed as follows: StepA4.

1. Extract all noise level values ​​U in the data buffer. i ; StepA4.2, calculate the RMS value of all noise levels within the specified period; StepA4.3, at all noise level values ​​U i In the above equation, extract the maximum noise level value and record it as the noise peak value; The peak factor of the noise level in a given period is then calculated by dividing the peak noise level by the RMS value of all noise levels and recorded as FZ U ; StepA4.

4. Peak factor FZ of noise level U With the preset noise peak factor threshold FZY U For comparison: If FZ U >FZY U , it means that the noise level is in an abnormal state; If FZ U ≤FZY U , it means the noise level is in normal state; Step 4: According to the analysis result of the data feature analysis in step 3, the channel state is comprehensively determined, wherein the channel state includes the normal state and the abnormal state of the channel; The comprehensive judgment method is as follows: First pass: Calculate the comprehensive judgment value Q of the channel; Where W F , W E , W S and W U are respectively the preset weight coefficients corresponding to the data flow characteristics, data error rate characteristics, signal strength characteristics, and noise level characteristics, L F , L E , L S and L U They are the quantified values ​​of data flow characteristics, data error rate characteristics, signal strength characteristics, and noise level characteristics respectively; Then, the channel status is determined according to the comprehensive judgment value Q of the channel; Step 5: Display the data flow characteristics, data error rate characteristics, signal strength characteristics, noise level characteristics within a specified period and the channel status determination results to relevant personnel.

2. A DOS system channel detection method according to claim 1, characterized in that: in, W F , W E , W S and W U The importance of each feature on the channel is set in advance according to experience, and W F +W E +W S +W U =1.

3. A DOS system channel detection method according to claim 1, characterized in that: The channel status is determined as follows: When Q ≥ 0, it means that the channel is in a normal state; When Q<0, it indicates that the channel is in an abnormal state.

4. A channel detection method for a DOS system according to claim 1, characterized in that: The method for determining the corresponding quantitative values ​​of the data flow characteristics, data error rate characteristics, signal strength characteristics, and noise level characteristics is as follows: When the data flow, data error rate, signal strength, and noise level are in normal state, 1 is used as the quantized value of the corresponding feature, that is, L F =1, L E =1, L S =1, L U =1; When the data flow, data error rate, signal strength, and noise level are in an abnormal state, -1 is used as the quantized value of the corresponding feature, that is, L F =-1, L E =-1, L S =-1, L U =-1.

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