CN119906533A - Time series data preprocessing system and method based on communication network - Google Patents
Time series data preprocessing system and method based on communication network Download PDFInfo
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Abstract
The invention relates to the field of signal processing, in particular to a time sequence data preprocessing system and method based on a communication network, wherein the system comprises a signal monitoring module, a wave frequency analysis module, a standard fitting module, a sequence processing module and a node scheduling module, wherein the signal monitoring module is used for expanding and storing a time sequence, the wave frequency analysis module is used for determining a time frequency window, cutting the sequence and calculating the composition of a subsequence, the standard fitting module is used for outputting a standardized sequence and an error interval, the sequence processing module is used for filtering a subsequent sequence, and the node scheduling module is used for scheduling an information processing task of a node.
Description
Technical Field
The invention relates to the field of signal processing, in particular to a time sequence data preprocessing system and method based on a communication network.
Background
A communication network is a system of communication links of a plurality of communication nodes and connecting nodes in order to enable the transmission and exchange of data information between the different nodes. In a communication network, signals are transmitted in a binary time sequence, wherein the time sequence is a series of data points arranged according to a time sequence, the change condition of information along with time is reflected, and the processing of the time sequence is beneficial to improving the data quality and optimizing the resource allocation among nodes.
Because the signal is easy to mix with noise in the transmission process, error codes exist in the restored time sequence signal during digital sampling, and the signal transmission precision is affected. Since the status codes, instruction sentences and the like in the communication network have the same expression form, the error codes can be filtered through signal analysis, but are limited by high instability of time sequences, and the error information is difficult to accurately distinguish by the prior processing technology.
In addition, each node in the communication network is composed of computers with different specifications, and has different hardware parameters, so that the processing capability and the adaptation capability of time series signals are different, and if the time series signals are not preprocessed, the node blockage phenomenon is easy to occur, and the operation efficiency of the communication network is affected.
Disclosure of Invention
The present invention is directed to a system and a method for preprocessing time-series data based on a communication network, so as to solve the problems set forth in the background art.
In order to solve the technical problems, the invention provides the technical scheme that the time sequence data preprocessing system based on the communication network comprises a signal monitoring module, a wave frequency analysis module, a standard fitting module, a sequence processing module and a node scheduling module;
The signal monitoring module is used for loading a monitoring control in a node server of a communication network, acquiring input data and output data of each communication network node, expanding the communication data in a time sequence, setting an information processing device or a signal transmission path at an input/output port of the node so as to store the expanded time sequence, and carrying out operation analysis on the time sequence;
the wave frequency analysis module is used for processing the expanded time sequence by adopting windowing transformation of the variable window, determining a time-frequency window of the time sequence according to the window interval when the correlation coefficient is highest, cutting the sequence according to the time-frequency window to obtain subsequences, carrying out dimensionless broken line transformation on each subsequence and the preamble subsequence, and calculating the composition difference, the composition ratio and the T-shaped correlation coefficient of each subsequence according to the information distribution among the subsequences;
The standard fitting module is used for calculating the association degree of each subsequence with other subsequences according to the T-shaped association coefficient among the subsequences, reserving all sequence sets with the association degree smaller than a threshold value to obtain a first reference set, discarding the sets with the sequence number smaller than the threshold value in the first reference set to obtain second reference sets, performing differential fitting on all the subsequences in each second reference set, and outputting a standardized sequence and an observation error interval corresponding to each second reference set;
the sequence processing module is used for acquiring a subsequent input or output sequence, separating a subsequent subsequence according to a time-frequency window cutting sequence, calculating the association degree of the subsequent subsequence and each standardized sequence, carrying out standardized processing on the subsequent sequence when the association degree is positioned in an error interval, enabling the subsequent sequence to be consistent with the standardized fitting sequence and then outputting, otherwise repeating time window cutting, and fusing a cutting result into a first reference set;
the node scheduling module is used for acquiring the number of the input and output subsequences of each node in the communication network, determining the operation speed of the node according to the ratio of the number of the input and output subsequences, and carrying out tasking node management on the communication network according to the operation speed of the node so as to keep the number of the input and output subsequences of each node in a uniform ratio to stabilize the data processing efficiency of the communication network.
Further, the signal monitoring module comprises a port control unit and a central processing unit;
the port control unit is arranged in an input/output port of the node server and is used for monitoring the time sequence of the input/output signals;
The central processing unit is used for providing calculation force support for time sequence analysis through a terminal data operation chip or a feedback path.
Further, the wave frequency analysis module comprises a windowing conversion unit, a time-frequency cutting unit and a composition analysis unit;
The windowing transformation unit is used for selecting a window function according to the data information entropy and performing discrete wavelet transformation on the time sequence based on the window function;
The time-frequency cutting unit is used for calculating the time delay of a window function when the discrete wavelet transformation function takes the maximum value, and cutting the sequence by using a time-frequency window to obtain a subsequence;
The composition analysis unit is used for calculating composition differences, composition ratios and T-type correlation coefficient functions of the subsequences according to information distribution among the subsequences.
Further, the standard fitting module comprises a correlation grouping unit and a differential fitting unit;
The association grouping unit is used for calculating the association degree of the T-shaped association coefficient among the subsequences and grouping the subsequences according to the calculation result;
the difference fitting unit is used for screening grouping results, removing invalid groupings and non-significant groupings, and obtaining a second reference set.
Further, the sequence processing module comprises a signal filtering unit and a sequence fusion unit;
The signal filtering unit is used for cutting the subsequent subsequence and filtering the subsequent subsequence according to the corresponding standard sequence information;
The sequence fusion unit is used for fusing the filtered subsequent subsequences, reducing the subsequent subsequences into a time sequence and sending the time sequence to the communication node for processing.
Further, the node scheduling module comprises a node testing unit, a task management unit and an efficiency stabilizing unit;
The node test unit is used for calculating the information processing speed of the communication node according to the proportion of the information quantity of the input subsequence and the output subsequence in the communication node;
the task management unit is used for planning an optimal task allocation mode according to the data volume of the signal to be transmitted and the information processing speed of each node;
The efficiency stabilizing unit is used for calculating the overall efficiency of the communication network in real time and uploading the information quantity of the input and output time sequence of the communication network in real time.
The time sequence data preprocessing method based on the communication network comprises the following steps:
S1, monitoring input and output signals at a port of a node server, expanding the signals in a time sequence form, calculating information entropy of the time sequence, selecting a window function according to the information entropy, and performing discrete wavelet transform on the time sequence by taking the window function as a fundamental frequency to obtain a discrete wavelet transform function;
s2, calculating the time delay of a window function when the discrete wavelet transformation function takes the maximum value, recording the time delay as a time-frequency window, taking the time-frequency window as a cutting time sequence, obtaining subsequences with fixed code element length, carrying out dimensionless broken line transformation on each subsequence, and calculating the composition difference, the composition ratio and the T-shaped association coefficient of each subsequence;
S3, calculating T-type association coefficients between each subsequence and other subsequences, classifying all subsequences with the degree of association smaller than a threshold value into one type to obtain a first reference set, and discarding the set with the number of elements smaller than the threshold value to obtain a second reference set;
s4, superposing and sampling sub-sequences in each reference set to obtain a standardized sequence and an error interval, cutting a subsequent time sequence according to a time-frequency window, calculating T-shaped association coefficients of the sub-sequences of the subsequent time sequence and each standardized sequence, and carrying out standardized processing on each subsequent sub-sequence when the association degree is in the error interval;
s5, representing the information processing speed of the communication node according to the information quantity ratio of the input subsequence and the output subsequence in the communication node, and performing task allocation according to the data quantity of the signal to be transmitted and the information processing speed of each node so as to maximize the overall efficiency of the communication network.
Further, step S1 includes:
S11, loading a monitoring control in a node server of a communication network, monitoring input and output signals, and transmitting the input and output signals into a processing chip through a terminal data operation chip or a feedback path;
S12, expanding the monitored input and output signals into a time sequence in a processing chip, wherein the time sequence is a finite length sequence, elements in the sequence are binary, calculating information entropy according to the length of the time sequence, and selecting a window function from a preloaded function library according to the information entropy;
s13, performing discrete wavelet transformation on the time sequence by taking a window function as a fundamental frequency, wherein the transformation function is as follows:
;
Wherein f (a, b) is a discrete wavelet transform function, a is a scale parameter, b is a time delay parameter, t0 is a sampling interval of a time sequence, delta (k) is a window function, E (k) is a kth element value in the time sequence, N is the number of elements in the time sequence, and k is a sequence index.
Further, step S2 includes:
S21, limiting the scale parameter in a period length range of a window function, calculating the maximum value of a discrete wavelet transformation function, and recording the size of a time delay parameter b when the maximum value is taken as an output function as a time frequency window;
S22, cutting a time sequence by using a time-frequency window, enabling each subsequence to contain m elements, complementing the subsequence with a low-level signal if the subsequence with the length less than m exists, numbering the subsequences according to a time sequence, determining the preamble sequence of each subsequence according to a descending order of the numbering, and taking the last subsequence as the preamble sequence in the initial subsequence;
Carrying out dimensionless polyline transformation on each sub-sequence, and calculating the composition difference, composition ratio and T-type association coefficient of each sub-sequence:
;
Wherein z represents the composition difference between the subsequence and the preamble sequence, z1 (x) represents the difference between the xth element and the xth+1th element in the preamble subsequence, z2 (x) represents the difference between the xth element and the xth+1th element in the current subsequence, s represents the composition ratio of the subsequence and the preamble sequence, min and max represent maximum and minimum functions, respectively, and r represents a T-type correlation coefficient.
Further, step S3 includes:
S31, calculating T-type association coefficients between all subsequences and other sequences, dividing the subsequences into a group, enabling the association coefficients between every two sequences in the group to be smaller than a threshold value, storing all groups into a set, and obtaining a first reference set;
And S32, screening out all the packets with the sequence number smaller than the threshold value in the first reference set to obtain a second reference set.
Further, step S4 includes:
S41, calculating the average value of each bit element in all subsequences in the group for each group in the second reference set, compiling into a high-level signal if the average value is larger than a sampling standard, compiling into a low-level signal if the average value is smaller than the sampling standard, acquiring all compiled signals, and combining the signals into a standardized sequence, wherein an error interval is [ - Σ (x=1,m)|z(x)-z0(x)|,Σ(x=1,m) |z (x) -z0 (x) |], z (x) represents an xth element in the standardized sequence, and z0 (x) represents the average value of xth element in all subsequences in the group;
S42, when the subsequent time sequences are transmitted in the communication node, cutting the subsequent time sequences according to a time-frequency window to obtain subsequences of the subsequent time sequences, calculating T-shaped association coefficients between the subsequences of each subsequent time sequence and the standardized sequence, and if the association degree is within an error interval, replacing the subsequences of the subsequent time sequences with the standardized sequence, and restoring and outputting the result.
Further, step S5 includes:
S51, determining the information processing speed of the communication node according to the proportion of the information quantity of the input subsequence and the output subsequence in the communication node, and marking the speed of each node in a communication network;
S52, calculating information quantity of data to be processed, and distributing flow among nodes by using a scheduling algorithm, wherein the scheduling algorithm comprises dynamic weighted scheduling, hybrid scheduling, neural network scheduling and batch processing scheduling.
Compared with the prior art, the invention has the following beneficial effects:
According to the invention, communication data is expanded in a time sequence, the windowing transformation of a variable window is adopted, the time-frequency window of the time sequence is determined, the dimensionless polyline transformation is carried out on each sequence and the preamble sequence according to the time-frequency window cutting sequence, the T-shaped association coefficient of the technical sequence can identify the long-term trend, the periodic variation and the seasonal pattern in the data, the cyclic pattern of the time sequence can be predicted more accurately, and the integrity and the quality of the data are improved.
According to the method, the relevance is calculated according to the T-shaped relevance coefficient, all sequence sets with relevance smaller than the threshold value are reserved, when the subsequent input is positioned in an error interval of the standardized sequence, the subsequent sequence is subjected to standardized pretreatment and then output, and abnormal noise in data is identified and removed through a smoothing technology, so that the time sequence is clearer and has better interpretability.
The invention determines the operation speed of the nodes according to the input-output standardized sequence number of each node and the input-output ratio, performs task node management on the communication network, and ensures that the input-output sequence number of each node keeps a uniform ratio so as to stabilize the data processing efficiency of the communication network, better understand the dynamic information processing state of the communication network and improve the overall information processing efficiency of the communication network.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, serve to explain the invention. In the drawings:
FIG. 1 is a schematic diagram of a communication network-based time series data preprocessing system according to the present invention;
fig. 2 is a schematic diagram of steps of a time-series data preprocessing method based on a communication network according to the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but 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.
Referring to fig. 1, the invention provides a time sequence data preprocessing system based on a communication network, which comprises a signal monitoring module, a wave frequency analysis module, a standard fitting module, a sequence processing module and a node scheduling module;
The signal monitoring module is used for loading a monitoring control in a node server of a communication network, acquiring input data and output data of each communication network node, expanding the communication data in a time sequence, setting an information processing device or a signal transmission path at an input/output port of the node so as to store the expanded time sequence, and carrying out operation analysis on the time sequence;
The signal monitoring module comprises a port control unit and a central processing unit;
the port control unit is arranged in an input/output port of the node server and is used for monitoring the time sequence of the input/output signals;
The central processing unit is used for providing calculation force support for time sequence analysis through a terminal data operation chip or a feedback path.
The wave frequency analysis module is used for processing the expanded time sequence by adopting windowing transformation of the variable window, determining a time-frequency window of the time sequence according to the window interval when the correlation coefficient is highest, cutting the sequence according to the time-frequency window to obtain subsequences, carrying out dimensionless broken line transformation on each subsequence and the preamble subsequence, and calculating the composition difference, the composition ratio and the T-shaped correlation coefficient of each subsequence according to the information distribution among the subsequences;
The wave frequency analysis module comprises a windowing conversion unit, a time-frequency cutting unit and a constituent analysis unit;
The windowing transformation unit is used for selecting a window function according to the data information entropy and performing discrete wavelet transformation on the time sequence based on the window function;
The time-frequency cutting unit is used for calculating the time delay of a window function when the discrete wavelet transformation function takes the maximum value, and cutting the sequence by using a time-frequency window to obtain a subsequence;
The composition analysis unit is used for calculating composition differences, composition ratios and T-type correlation coefficient functions of the subsequences according to information distribution among the subsequences.
The standard fitting module is used for calculating the association degree of each subsequence with other subsequences according to the T-shaped association coefficient among the subsequences, reserving all sequence sets with the association degree smaller than a threshold value to obtain a first reference set, discarding the sets with the sequence number smaller than the threshold value in the first reference set to obtain second reference sets, performing differential fitting on all the subsequences in each second reference set, and outputting a standardized sequence and an observation error interval corresponding to each second reference set;
the standard fitting module comprises a correlation grouping unit and a differential fitting unit;
The association grouping unit is used for calculating the association degree of the T-shaped association coefficient among the subsequences and grouping the subsequences according to the calculation result;
the difference fitting unit is used for screening grouping results, removing invalid groupings and non-significant groupings, and obtaining a second reference set.
The sequence processing module is used for acquiring a subsequent input or output sequence, separating a subsequent subsequence according to a time-frequency window cutting sequence, calculating the association degree of the subsequent subsequence and each standardized sequence, carrying out standardized processing on the subsequent sequence when the association degree is positioned in an error interval, enabling the subsequent sequence to be consistent with the standardized fitting sequence and then outputting, otherwise repeating time window cutting, and fusing a cutting result into a first reference set;
The sequence processing module comprises a signal filtering unit and a sequence fusion unit;
The signal filtering unit is used for cutting the subsequent subsequence and filtering the subsequent subsequence according to the corresponding standard sequence information;
The sequence fusion unit is used for fusing the filtered subsequent subsequences, reducing the subsequent subsequences into a time sequence and sending the time sequence to the communication node for processing.
The node scheduling module is used for acquiring the number of the input and output subsequences of each node in the communication network, determining the operation speed of the node according to the ratio of the number of the input and output subsequences, and carrying out tasking node management on the communication network according to the operation speed of the node so as to keep the number of the input and output subsequences of each node in a uniform ratio to stabilize the data processing efficiency of the communication network.
The node scheduling module comprises a node testing unit, a task management unit and an efficiency stabilizing unit;
The node test unit is used for calculating the information processing speed of the communication node according to the proportion of the information quantity of the input subsequence and the output subsequence in the communication node;
the task management unit is used for planning an optimal task allocation mode according to the data volume of the signal to be transmitted and the information processing speed of each node;
The efficiency stabilizing unit is used for calculating the overall efficiency of the communication network in real time and uploading the information quantity of the input and output time sequence of the communication network in real time.
As shown in fig. 2, the communication network-based time series data preprocessing method includes the following steps:
S1, monitoring input and output signals at a port of a node server, expanding the signals in a time sequence form, calculating information entropy of the time sequence, selecting a window function according to the information entropy, and performing discrete wavelet transform on the time sequence by taking the window function as a fundamental frequency to obtain a discrete wavelet transform function;
the step S1 comprises the following steps:
S11, loading a monitoring control in a node server of a communication network, monitoring input and output signals, and transmitting the input and output signals into a processing chip through a terminal data operation chip or a feedback path;
S12, expanding the monitored input and output signals into a time sequence in a processing chip, wherein the time sequence is a finite length sequence, elements in the sequence are binary, calculating information entropy according to the length of the time sequence, and selecting a window function from a preloaded function library according to the information entropy;
s13, performing discrete wavelet transformation on the time sequence by taking a window function as a fundamental frequency, wherein the transformation function is as follows:
;
Wherein f (a, b) is a discrete wavelet transform function, a is a scale parameter, b is a time delay parameter, t0 is a sampling interval of a time sequence, delta (k) is a window function, E (k) is a kth element value in the time sequence, N is the number of elements in the time sequence, and k is a sequence index.
S2, calculating the time delay of a window function when the discrete wavelet transformation function takes the maximum value, recording the time delay as a time-frequency window, taking the time-frequency window as a cutting time sequence, obtaining subsequences with fixed code element length, carrying out dimensionless broken line transformation on each subsequence, and calculating the composition difference, the composition ratio and the T-shaped association coefficient of each subsequence;
The step S2 comprises the following steps:
S21, limiting the scale parameter in a period length range of a window function, calculating the maximum value of a discrete wavelet transformation function, and recording the size of a time delay parameter b when the maximum value is taken as an output function as a time frequency window;
S22, cutting a time sequence by using a time-frequency window, enabling each subsequence to contain m elements, complementing the subsequence with a low-level signal if the subsequence with the length less than m exists, numbering the subsequences according to a time sequence, determining the preamble sequence of each subsequence according to a descending order of the numbering, and taking the last subsequence as the preamble sequence in the initial subsequence;
Carrying out dimensionless polyline transformation on each sub-sequence, and calculating the composition difference, composition ratio and T-type association coefficient of each sub-sequence:
;
Wherein z represents the composition difference between the subsequence and the preamble sequence, z1 (x) represents the difference between the xth element and the xth+1th element in the preamble subsequence, z2 (x) represents the difference between the xth element and the xth+1th element in the current subsequence, s represents the composition ratio of the subsequence and the preamble sequence, min and max represent maximum and minimum functions, respectively, and r represents a T-type correlation coefficient.
S3, calculating T-type association coefficients between each subsequence and other subsequences, classifying all subsequences with the degree of association smaller than a threshold value into one type to obtain a first reference set, and discarding the set with the number of elements smaller than the threshold value to obtain a second reference set;
the step S3 comprises the following steps:
S31, calculating T-type association coefficients between all subsequences and other sequences, dividing the subsequences into a group, enabling the association coefficients between every two sequences in the group to be smaller than a threshold value, storing all groups into a set, and obtaining a first reference set;
And S32, screening out all the packets with the sequence number smaller than the threshold value in the first reference set to obtain a second reference set.
S4, superposing and sampling sub-sequences in each reference set to obtain a standardized sequence and an error interval, cutting a subsequent time sequence according to a time-frequency window, calculating T-shaped association coefficients of the sub-sequences of the subsequent time sequence and each standardized sequence, and carrying out standardized processing on each subsequent sub-sequence when the association degree is in the error interval;
The step S4 includes:
S41, calculating the average value of each bit element in all subsequences in the group for each group in the second reference set, compiling into a high-level signal if the average value is larger than a sampling standard, compiling into a low-level signal if the average value is smaller than the sampling standard, acquiring all compiled signals, and combining the signals into a standardized sequence, wherein an error interval is [ - Σ (x=1,m)|z(x)-z0(x)|,Σ(x=1,m) |z (x) -z0 (x) |], z (x) represents an xth element in the standardized sequence, and z0 (x) represents the average value of xth element in all subsequences in the group;
S42, when the subsequent time sequences are transmitted in the communication node, cutting the subsequent time sequences according to a time-frequency window to obtain subsequences of the subsequent time sequences, calculating T-shaped association coefficients between the subsequences of each subsequent time sequence and the standardized sequence, and if the association degree is within an error interval, replacing the subsequences of the subsequent time sequences with the standardized sequence, and restoring and outputting the result.
S5, representing the information processing speed of the communication node according to the information quantity ratio of the input subsequence and the output subsequence in the communication node, and performing task allocation according to the data quantity of the signal to be transmitted and the information processing speed of each node so as to maximize the overall efficiency of the communication network.
The step S5 comprises the following steps:
S51, determining the information processing speed of the communication node according to the proportion of the information quantity of the input subsequence and the output subsequence in the communication node, and marking the speed of each node in a communication network;
S52, calculating information quantity of data to be processed, and distributing flow among nodes by using a scheduling algorithm, wherein the scheduling algorithm comprises dynamic weighted scheduling, hybrid scheduling, neural network scheduling and batch processing scheduling.
In the embodiment, the time sequence of the input communication node is [10110110], the time domain window is 3 after windowing transformation, the sequence is cut into three subsequences of [101], [101] and [100], the [101] and the [101] are divided into one group, the [100] is divided into one group, the grouping of the [100] is screened, and when the subsequent signal input is [111], the subsequent signal is normalized to be the [101] output.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
It should be noted that the above-mentioned embodiments are merely preferred embodiments of the present invention, and the present invention is not limited thereto, but may be modified or substituted for some of the technical features thereof by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
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