CN117318729A - Parameter management system for underground explosion-proof electrical equipment of coal mine - Google Patents
Parameter management system for underground explosion-proof electrical equipment of coal mine Download PDFInfo
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- H03M—CODING; DECODING; CODE CONVERSION IN GENERAL
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Abstract
The invention relates to the technical field of data compression, in particular to a parameter management system of underground explosion-proof electrical equipment of a coal mine, which comprises the following components: the data acquisition module is used for acquiring data sequences of all parameters of the underground coal mine explosion-proof electrical equipment; the data selection module is used for obtaining two target sequences according to the difference degree of any two data sequences in the data sequences of all the parameters; the data analysis module is used for acquiring the priority of each data in the target sequence; and the data compression module is used for compressing all parameters of the underground coal mine explosion-proof electrical equipment except the target sequence according to the priority of each data in the target sequence, and storing and managing all compression results. The invention achieves data compression by preferentially compressing important data, thereby reducing the possibility of important data loss.
Description
Technical Field
The invention relates to the technical field of data compression, in particular to a parameter management system for underground explosion-proof electrical equipment of a coal mine.
Background
Coal is used as an important energy support in China, and great demands exist for the use of coal. Coal mine resources are typically stored underground and require mine operations when the resources are mined. In the case of resource exploitation, dangerous gases or suspended coal particles may exist in the complex interior of a mine, and the electrical equipment used under the mine needs to be explosion-proof equipment. And recording parameter information in the using process of the explosion-proof equipment, and monitoring the running state of the equipment in real time, wherein the equipment generates larger data volume, so that the acquired data is required to be compressed.
In the traditional LZW compression algorithm, the compression rate of data with larger repeated data proportion in the data to be compressed is high, the compression rate of data with smaller repeated data proportion is low, the data with normal parameters of the underground explosion-proof electrical equipment in the coal mine is large in proportion, the abnormal data is small in proportion, the importance of the abnormal data is far greater than that of the normal data, the data cannot be compressed according to the importance degree of the data by the traditional LZW compression algorithm, and therefore the important data cannot be well stored.
Disclosure of Invention
The invention provides a parameter management system for underground explosion-proof electrical equipment of a coal mine, which aims to solve the existing problems.
The invention relates to a parameter management system for underground explosion-proof electrical equipment of a coal mine, which adopts the following technical scheme:
the method comprises the following modules:
the data acquisition module is used for acquiring all parameters of the underground coal mine explosion-proof electrical equipment and obtaining a data sequence of all parameters of the underground coal mine explosion-proof electrical equipment;
the data selection module is used for obtaining two target sequences according to the difference degree of any two data sequences in the data sequences of all the parameters;
the data analysis module is used for acquiring historical data of the target sequence; constructing historical curves of a plurality of target sequences according to the historical data of the plurality of target sequences; acquiring a plurality of groups of frequency curves according to historical curves of a plurality of target sequences; acquiring regularity of each frequency curve according to a plurality of groups of frequency curves; acquiring two interfered frequency curves according to the regularity of each frequency curve and the frequency of each frequency curve; acquiring a reference value curve of the target sequence according to the two interfered frequency curves; acquiring the priority of each data in the target sequence according to the reference value curve of the target sequence;
and the data compression module is used for compressing all parameters of the underground coal mine explosion-proof electrical equipment except the target sequence according to the priority of each data in the target sequence, and storing and managing all compression results.
Preferably, the method for obtaining two target sequences according to the difference degree of any two data sequences in the data sequences of all parameters includes the following specific steps:
by calculating absolute values of pearson correlation coefficients between data sequences of various parameters and recording the obtained absolute value set of pearson correlation coefficients asWherein->Number of data sequences representing all parameters, +.>Indicate->Data sequence of seed parameters and +.>Correlation between data sequences of seed parametersAnd obtaining the difference degree of any two data sequences according to the absolute value set of the pearson correlation coefficient, and marking the two data sequences with the largest difference degree as target sequences.
Preferably, the specific calculation formula for obtaining the difference degree of any two data sequences from the absolute value set of pearson correlation coefficients is as follows:
in the method, in the process of the invention,indicate->Data sequence of seed parameters and +.>Degree of difference in data sequence of seed parameters, +.>Indicate->Data sequence of seed parameters and +.>Correlation between data sequences of seed parameters; />Indicating removal of->Data sequence of seed parameters and +.>After correlation between data sequences of seed parameters, the average value of absolute values of all pearson correlation coefficients in the absolute value set of pearson correlation coefficients; />An exponential function based on a natural constant is represented.
Preferably, the method for obtaining the plurality of groups of frequency curves includes the following specific steps:
and decomposing all target sequence history curves by using EMD to obtain a plurality of groups of frequency curves, wherein a plurality of frequency curves exist in each group of frequency curves.
Preferably, the method for obtaining the regularity of each frequency curve includes the following specific steps:
and (3) obtaining the similarity between the curves of the same frequency in different groups by using a DTW algorithm on the curves of the same frequency in all groups, obtaining a similarity set formed by the similarities between the curves of the same frequency in pairs, and obtaining the regularity of each frequency through the variance and the mean value of each similarity set.
Preferably, the regularity of each frequency is obtained by the variance and the mean of each similarity set, and the specific calculation formula is:
in the method, in the process of the invention,indicate->Regularity of the individual frequency curves; />Indicate->Average value of similarity in each frequency curve set; />First->Variance of similarity in the set of individual frequency curves; />An exponential function based on a natural constant;representing the normalization function.
Preferably, the acquiring two interfered frequency curves includes the following specific calculation formulas:
in the method, in the process of the invention,indicate->Regularity of the individual frequency curves +.>Indicate->Frequency of the individual frequency curve, +.>Represent the firstDegree of regularity of the individual frequency curves +.>Indicate->Regularity of the individual frequency curves +.>Indicate->The frequency of the individual frequency curve is such that,indicate->The disturbance degree of each frequency curve is respectively selected from +.>And->The frequency curve with the largest value is taken as the two frequency curves which are obtained and interfered.
Preferably, the method for obtaining the reference value curve of the target sequence includes the following specific steps:
removing the two interfered frequency curves from all the frequency curves in all the groups to obtain the rest frequency curves in all the groups; and carrying out EMD reconstruction on the residual frequency curves to obtain a plurality of reconstruction curves, and taking the average value of the plurality of reconstruction curves as a reference value curve of the target sequence.
Preferably, the method for acquiring the priority of each data in the target sequence includes the following specific steps:
normalizing the difference between the data in the acquired target sequence and the reference value of the target sequence to obtain the difference degree between the data in the acquired target sequence and the reference value of the target sequence, and presetting a difference degree threshold valueWhen the degree of difference between the data in the collected target sequence and the reference value of the target sequence is greater than or equal to +.>When the data in the acquired target sequence is abnormal data; when the degree of difference between the data in the acquired target sequence and the reference value of the target sequence is less than +.>When then the number in the acquired target sequenceThe data is normal data;
for the abnormal data, the priority of the abnormal data is set to 1; for normal data, the frequency of occurrence of the normal data in the target sequence is taken as the priority of the normal data.
Preferably, the method for compressing parameters of all underground coal mine explosion-proof electrical equipment except the target sequence according to the priority of each data in the target sequence, and storing and managing all compression results includes the following specific steps:
the method comprises the steps of performing descending order sequencing on the priorities of data in two target sequences to form two priority sequences, taking the average value of the two priority sequences as an overall priority sequence, acquiring the acquisition time of the data in the overall priority sequence to form an acquisition time sequence, arranging the data in the data sequences of other underground coal mine explosion-proof electrical equipment parameters according to the arrangement sequence of the acquisition time in the acquisition time sequence to obtain new data sequences of other underground coal mine explosion-proof electrical equipment parameters, performing LZW coding compression on the data sequences of the underground coal mine explosion-proof electrical equipment parameters, and obtaining compression results of the data sequences of all parameters of the underground coal mine explosion-proof electrical equipment.
The technical scheme of the invention has the beneficial effects that: because the compression rate of the data with larger repeated data proportion in the data to be compressed in the traditional LZW compression algorithm is high, the compression rate of the data with smaller repeated data proportion is low, the data with normal parameters of the underground explosion-proof electrical equipment in the coal mine is large in proportion, the abnormal data is small in proportion, the importance of the abnormal data is far greater than that of the normal data, the data cannot be compressed according to the importance degree of the data in the traditional LZW compression algorithm, and therefore the important data cannot be well stored.
According to the invention, the data with high priority, namely the abnormal data, is compressed firstly according to the priority of the parameters of the underground explosion-proof electrical equipment of the coal mine, so that the data with high priority is well stored.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a block diagram of a system for managing parameters of an explosion-proof electrical device in a coal mine.
Detailed Description
In order to further describe the technical means and effects adopted by the invention to achieve the preset aim, the following detailed description is given below of a parameter management system for underground explosion-proof electrical equipment in coal mine according to the invention, which is provided by the invention, with reference to the accompanying drawings and the preferred embodiment. In the following description, different "one embodiment" or "another embodiment" means that the embodiments are not necessarily the same. Furthermore, the particular features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.
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 invention provides a concrete scheme of a parameter management system for underground explosion-proof electrical equipment of a coal mine, which is specifically described below with reference to the accompanying drawings.
Referring to fig. 1, a block diagram of a parameter management system for underground explosion-proof electrical equipment in a coal mine according to an embodiment of the invention is shown, the system includes the following modules:
the data acquisition module 101 acquires parameters of underground coal mine explosion-proof electrical equipment.
It should be noted that, the parameters of the explosion-proof electrical equipment in the underground coal mine include a plurality of data such as temperature, voltage, current and the like in the explosion-proof electrical equipment, and since the environment in the underground coal mine is stable under normal conditions, a large amount of repeated data exists in the parameters of the explosion-proof electrical equipment in the underground coal mine under normal conditions, and therefore the parameters of the explosion-proof electrical equipment in the underground coal mine need to be compressed, so the embodiment firstly compresses the parameters of the explosion-proof electrical equipment in the underground coal mine, and when compressing the parameters of the explosion-proof electrical equipment in the underground coal mine, the smaller the important data is compressed, the lower the possibility of losing the important data is.
Specifically, a plurality of data such as temperature, voltage, current and the like in parameters of underground coal mine explosion-proof electrical equipment are collected, and each pass is presetThe method comprises the steps of (1) grouping a plurality of data such as temperature, voltage and current in collected parameters of underground coal mine explosion-proof electrical equipment, and arranging various data in each group of collected parameters of underground coal mine explosion-proof electrical equipment along a time sequence to obtain a data sequence of all parameters of the underground coal mine explosion-proof electrical equipment, wherein ∈>The size of (2) can be set according to the actual situation, the hard requirement is not required in the present embodiment, and +.>Description will be made.
Thus, the acquisition of parameters of underground explosion-proof electrical equipment of the coal mine is completed.
The data selection module 102 calculates the relevance between the data sequences of various parameters according to various data sequences, and selects data according to the relevance between various data.
In this embodiment, as a method for compressing various parameters of an underground coal mine explosion-proof electrical device based on the LZW algorithm, an LZW dictionary capable of well compressing data sequences of various parameters needs to be formulated, and in order to formulate an LZW dictionary capable of well compressing data sequences of various parameters, correlations between the data sequences of various parameters need to be analyzed, and data sequences of parameters of the LZW dictionary capable of formulating the data sequences of various parameters are selected and recorded as target sequences.
Specifically, the pearson correlation between two pairs of data sequences by calculating various parametersAbsolute value of the number, and recording the obtained absolute value set of pearson correlation coefficients asWherein->Number of data sequences representing all parameters, +.>Indicate->Data sequence of seed parameters and +.>Absolute values of pearson correlation coefficients among data sequences of the parameters are obtained according to the absolute value set of the pearson correlation coefficients, and the difference degree of any two data sequences is obtained according to a specific calculation formula:
in the method, in the process of the invention,indicate->Data sequence of seed parameters and +.>The degree of difference of the data sequences of the seed parameters, abbreviated as +.>Seed and->Degree of difference of seed parameters,/->Indicate->Data sequence of seed parameters and +.>Correlation between data sequences of seed parameters; />Indicating removal of->Data sequence of seed parameters and +.>After correlation between data sequences of seed parameters, the average value of absolute values of all pearson correlation coefficients in the absolute value set of pearson correlation coefficients; />An exponential function based on a natural constant is represented.
It should be further noted that when calculatedThe greater the +.>Data sequence of seed parameters and +.>The higher the degree to which the data sequences of the seed parameters can characterize all the parameters, the two data sequences with the greatest degree to which all the parameters can be characterized are selected as target sequences.
Thus, a target sequence is obtained.
The data analysis module 103 obtains a reference value curve of the target sequence according to the target sequence, and obtains the priority of each data in the target sequence through the reference value curve of the target sequence.
It should be noted that, because there is a difference between the abnormal data and the normal data, whether the collected data is abnormal or not can be judged by the difference between the collected data and the history data; the collected data are collected at the current moment, and the historical data are collected in the history.
It should be further noted that, since two target sequences are obtained and the method and the process for obtaining the priorities of the two target sequences are identical, any one of the two target sequences is taken as an example for illustration.
Specifically, a reference value curve of the target sequence is obtained through historical data of the target sequence, the historical obtained reference judges abnormal conditions of the data collected at present, and the abnormal conditions are analyzed according to deviation conditions of the data collected at present and the reference data.
It should be further noted that, because the underground environment information of the coal mine is special, the environmental change may affect the calculation of the processing terminal, and the underground electric equipment of the coal mine may generate mechanical vibration during operation, and may also generate earthquake impact, these physical disturbances may affect the stability of the equipment connection to affect the parameters of the explosion-proof electric equipment collected by the sensor, but the underground electric equipment of the coal mine may generate mechanical vibration during operation to cause high regularity, and the impact caused by earthquake impact is low, so the reference value curve of the target sequence is obtained based on this.
Specifically, the target sequence is acquired in the order from the back to the frontThe history data is constructed by taking time as a horizontal axis and taking the history data in a target sequence as a vertical axis>History of the individual target sequences, wherein->The value preset for this implementation is +.in this example>And->The specific value of the target sequence history curve can be set according to the actual situation, the specific requirement is not made in the embodiment, and each target sequence history curve is taken as a group; using EMD to decompose all target sequence history curves, wherein EMD decomposition is a well-known technique, so in this embodiment, the description is omitted to obtain ∈ ->A set of frequency curves, wherein each set of frequency curves has a plurality of frequency curves;
the similarity between the curves of the same frequency in different groups is obtained by using a DTW algorithm on the curves of the same frequency in all groups, wherein the DTW algorithm is a known existing algorithm, and no redundant description is made in the embodiment; the similarity set formed by obtaining the similarity between the same frequency curves is recorded asThe regularity of each frequency is obtained through the variance and the mean value of each similarity set, and a specific calculation formula is as follows:
in the method, in the process of the invention,indicate->Regularity of the individual frequency curves; />Indicate->Average value of similarity in each frequency curve set; />First->Variance of similarity in the set of individual frequency curves; />An exponential function based on a natural constant;representing a Softmax normalization function.
It should be further noted that, the greater the regularity of the calculated frequency curve, the more regular the distribution of the calculated frequency curve, and the smaller the regularity of the calculated frequency curve, the more irregular the distribution of the calculated frequency curve. Meanwhile, because the impact generated by the mechanical vibration and the earthquake in the embodiment is mainly distributed in the high-frequency information, the frequency curve influenced by the impact generated by the mechanical vibration and the earthquake is also needed to be obtained by combining the frequency in the frequency curve.
Specifically, the frequency curve affected by the impact generated by the mechanical vibration and the earthquake is obtained through the regularity of each frequency curve and the frequency of each frequency curve, and the specific calculation formula is as follows:
in the method, in the process of the invention,indicate->Regularity of the individual frequency curves +.>Indicate->Frequency of the individual frequency curve, +.>Represent the firstDegree of regularity of the individual frequency curves +.>Indicate->Regularity of the individual frequency curves +.>Indicate->The frequency of the individual frequency curve is such that,indicate->Degree of turbulence of the individual frequency curves.
Further, the degree of regularity of the influence due to the mechanical vibration is high, and the degree of disturbance of the influence due to the impact of the earthquake is high; the frequency curve with the greatest degree of regularity is used as a frequency curve caused by mechanical vibration, the frequency curve with the greatest degree of turbulence is used as a frequency curve caused by earthquake impact, and the reference value curve of the target sequence is obtained through the frequency curve caused by earthquake impact and the frequency curve caused by mechanical vibration.
Specifically, the frequency curve affected by the mechanical vibration and the frequency curve affected by the seismic impact are calculated fromRemoving from all frequency curves in each group to obtain +.>Remaining frequency curves within the respective groups; EMD reconstruction is performed on the remaining frequency curve to obtain +.>A reconstruction curve, in which EMD reconstruction is a well-known technique, is not described in detail in this embodiment; for->The mean value of the individual reconstruction curves serves as a reference value for the target sequence.
Thus, a reference value of the target sequence is acquired.
It should be noted that, when the difference between the data in the collected target sequence and the reference value of the target sequence is too large, the collected data is considered to be abnormal data, otherwise, the collected data is considered to be normal data, and meanwhile, the importance of the abnormal data is far greater than that of the normal data, so that the priority of the abnormal data is far greater than that of the normal data.
Specifically, the difference degree between the data in the collected target sequence and the reference value of the target sequence is obtained by carrying out linear normalization on the difference between the data in the collected target sequence and the reference value of the target sequence, and then a difference degree threshold value is presetCan be set according to the specific situation>In the embodiment, 0.7 is described, when the difference degree between the data in the collected target sequence and the reference value of the target sequence is greater than or equal to +.>When the data in the acquired target sequence is considered to be abnormal data, the priority of the acquired data needs to be improved; the specific calculation formula is as follows:
in the method, in the process of the invention,for the +.>Data of->The first of the reference value curves for the target sequenceData of->For the degree of difference between the data in the acquired target sequence and the reference value of the target sequence +.>For the preset difference threshold value, the +.>The value of (2) is not specifically required in this embodiment, and 0.7 is described in this embodiment>Representing a Softmax function for achieving normalization;
when the degree of difference between the data in the acquired target sequence and the reference value of the target sequence is greater than or equal toWhen the data in the acquired target sequence is considered to be abnormal data, the degree of difference between the data in the acquired target sequence and the reference value of the target sequence is less than +.>When the data in the acquired target sequence is considered to be normal data;
for the abnormal data, the priority of the abnormal data is set to 1; for normal data, the frequency of occurrence of the normal data in the target sequence is taken as the priority of the normal data.
So far, the priority of each data in the acquired target sequence is obtained.
The data compression module 104 compresses parameters of the underground coal mine explosion-proof electrical equipment according to the priority of each data in the target sequence.
Since the two target sequences are the two data sequences with the lowest correlation between the various data sequences, the correlation between any data sequence in the remaining data sequences and the target sequences is necessarily larger than the correlation between the two target sequences, so that the average value of the priorities of the data in the two target sequences is used as the priority of the data in the remaining data sequences and is recorded as the overall priority.
It should be further explained that, because the importance of the abnormal data is far greater than that of the normal data, the embodiment compresses various data in parameters of the underground explosion-proof electrical equipment of the coal mine according to the priority of each data when the LZW data is compressed.
Specifically, the priorities of the data in the two target sequences are ordered in a descending order to form two priority sequences, the average value of the two priority sequences is taken as the whole priority sequence, the acquisition time of the data in the whole priority sequence is acquired to form an acquisition time sequence, the data in the data sequences of other underground coal mine explosion-proof electrical equipment parameters are arranged according to the arrangement sequence of the acquisition time in the acquisition time sequence, the data sequences of the new other underground coal mine explosion-proof electrical equipment parameters are obtained, the data sequences of the underground coal mine explosion-proof electrical equipment parameters are subjected to LZW coding compression, and the compression result of the data sequences of all the parameters of the underground coal mine explosion-proof electrical equipment is obtained.
Thus, the compression of parameters of underground explosion-proof electrical equipment of the coal mine is completed.
According to the embodiment, the priority of various data in the underground coal mine anti-explosion electrical equipment parameters is used as the sequence, the underground coal mine anti-explosion electrical equipment parameters are compressed, so that the data with high priority are compressed first, namely, the possibility of abnormal data loss is achieved, and the purpose of well storing the data with high priority is achieved.
This embodiment is completed.
The above description is only of the preferred embodiments of the present invention and is not intended to limit the invention, but any modifications, equivalent substitutions, improvements, etc. within the principles of the present invention should be included in the scope of the present invention.
Claims (10)
1. The parameter management system for the underground explosion-proof electrical equipment of the coal mine is characterized by comprising the following modules:
the data acquisition module is used for acquiring all parameters of the underground coal mine explosion-proof electrical equipment and obtaining a data sequence of all parameters of the underground coal mine explosion-proof electrical equipment;
the data selection module is used for obtaining two target sequences according to the difference degree of any two data sequences in the data sequences of all the parameters;
the data analysis module is used for acquiring historical data of the target sequence; constructing historical curves of a plurality of target sequences according to the historical data of the plurality of target sequences; acquiring a plurality of groups of frequency curves according to historical curves of a plurality of target sequences; acquiring regularity of each frequency curve according to a plurality of groups of frequency curves; acquiring two interfered frequency curves according to the regularity of each frequency curve and the frequency of each frequency curve; acquiring a reference value curve of the target sequence according to the two interfered frequency curves; acquiring the priority of each data in the target sequence according to the reference value curve of the target sequence;
and the data compression module is used for compressing all parameters of the underground coal mine explosion-proof electrical equipment except the target sequence according to the priority of each data in the target sequence, and storing and managing all compression results.
2. The system for managing parameters of underground explosion-proof electrical equipment in coal mine according to claim 1, wherein the two target sequences are obtained according to the difference degree of any two data sequences in the data sequences of all parameters, and the method comprises the following specific steps:
by calculating absolute values of pearson correlation coefficients between data sequences of various parameters and recording the obtained absolute value set of pearson correlation coefficients asWherein->Number of data sequences representing all parameters, +.>Indicate->Data sequence of seed parameters and +.>And (3) obtaining the degree of difference of any two data sequences according to the correlation between the data sequences of the seed parameters and the set of absolute values of the pearson correlation coefficients, and marking the two data sequences with the largest degree of difference as target sequences.
3. The system for managing parameters of underground coal mine explosion-proof electrical equipment according to claim 2, wherein the specific calculation formula for obtaining the difference degree of any two data sequences from the absolute value set of pearson correlation coefficients is as follows:
in the method, in the process of the invention,indicate->Data sequence of seed parameters and +.>Degree of difference in data sequence of seed parameters, +.>Indicate->Data sequence of seed parameters and +.>Correlation between data sequences of seed parameters; />Indicating removal of->Data sequence of seed parameters and +.>After correlation between data sequences of seed parameters, the average value of absolute values of all pearson correlation coefficients in the absolute value set of pearson correlation coefficients; />An exponential function based on a natural constant is represented.
4. The system for managing parameters of underground explosion-proof electrical equipment in coal mine as claimed in claim 1, wherein the method for obtaining the plurality of groups of frequency curves comprises the following specific steps:
and decomposing all target sequence history curves by using EMD to obtain a plurality of groups of frequency curves, wherein a plurality of frequency curves exist in each group of frequency curves.
5. The system for managing parameters of underground explosion-proof electrical equipment in coal mine according to claim 1, wherein the method for acquiring regularity of each frequency curve comprises the following specific steps:
and (3) obtaining the similarity between the curves of the same frequency in different groups by using a DTW algorithm on the curves of the same frequency in all groups, obtaining a similarity set formed by the similarities between the curves of the same frequency in pairs, and obtaining the regularity of each frequency through the variance and the mean value of each similarity set.
6. The system for managing parameters of underground coal mine explosion-proof electrical equipment according to claim 5, wherein the regularity of each frequency is obtained by the variance and the mean of each similarity set, and the specific calculation formula is as follows:
in the method, in the process of the invention,indicate->Regularity of the individual frequency curves; />Indicate->Average value of similarity in each frequency curve set;first->Variance of similarity in the set of individual frequency curves; />An exponential function based on a natural constant;representing the normalization function.
7. The system for managing parameters of underground explosion-proof electrical equipment in coal mine according to claim 1, wherein the acquiring of the two interfered frequency curves comprises the following specific calculation formulas:
in the method, in the process of the invention,indicate->Regularity of the individual frequency curves +.>Indicate->Frequency of the individual frequency curve, +.>Indicate->Degree of regularity of the individual frequency curves +.>Indicate->Regularity of the individual frequency curves +.>Indicate->Frequency of the individual frequency curve, +.>Indicate->The disturbance degree of each frequency curve is respectively selected from +.>And->The frequency curve with the largest value is taken as the two frequency curves which are obtained and interfered.
8. The system for managing parameters of underground explosion-proof electrical equipment in coal mine according to claim 1, wherein the method for obtaining the reference value curve of the target sequence comprises the following specific steps:
removing the two interfered frequency curves from all the frequency curves in all the groups to obtain the rest frequency curves in all the groups; and carrying out EMD reconstruction on the residual frequency curves to obtain a plurality of reconstruction curves, and taking the average value of the plurality of reconstruction curves as a reference value curve of the target sequence.
9. The system for managing parameters of underground explosion-proof electrical equipment in a coal mine according to claim 1, wherein the method for acquiring the priority of each data in the target sequence comprises the following specific steps:
normalizing the difference between the data in the acquired target sequence and the reference value of the target sequence to obtain the difference degree between the data in the acquired target sequence and the reference value of the target sequence, and presetting a difference degree threshold valueWhen the degree of difference between the data in the collected target sequence and the reference value of the target sequence is greater than or equal to +.>When the data in the acquired target sequence is abnormal data; when the degree of difference between the data in the acquired target sequence and the reference value of the target sequence is less than +.>When the data in the acquired target sequence is normal data;
for the abnormal data, the priority of the abnormal data is set to 1; for normal data, the frequency of occurrence of the normal data in the target sequence is taken as the priority of the normal data.
10. The system for managing parameters of explosion-proof electrical equipment in underground coal mine according to claim 1, wherein the method for compressing parameters of explosion-proof electrical equipment in underground coal mine except for the target sequence according to the priority of each data in the target sequence and storing and managing all compression results comprises the following specific steps:
the method comprises the steps of performing descending order sequencing on the priorities of data in two target sequences to form two priority sequences, taking the average value of the two priority sequences as an overall priority sequence, acquiring the acquisition time of the data in the overall priority sequence to form an acquisition time sequence, arranging the data in the data sequences of other underground coal mine explosion-proof electrical equipment parameters according to the arrangement sequence of the acquisition time in the acquisition time sequence to obtain new data sequences of other underground coal mine explosion-proof electrical equipment parameters, performing LZW coding compression on the data sequences of the underground coal mine explosion-proof electrical equipment parameters, and obtaining compression results of the data sequences of all parameters of the underground coal mine explosion-proof electrical equipment.
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Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20090064914A (en) * | 2007-12-17 | 2009-06-22 | 한국전자통신연구원 | Fine-granular scalability coding/decoding method and apparatus |
| CN114170334A (en) * | 2021-12-08 | 2022-03-11 | 北京欧铼德微电子技术有限公司 | Compensation data compression method and device, electronic equipment and storage medium |
| CN114579050A (en) * | 2022-02-14 | 2022-06-03 | 阿里巴巴（中国）有限公司 | Method and device for processing compressed data |
| CN115017252A (en) * | 2022-08-08 | 2022-09-06 | 深圳市航盛车云技术有限公司 | Intelligent playback system for vehicle track of mobile phone digital vehicle key |
| CN115935819A (en) * | 2022-12-08 | 2023-04-07 | 国电和风风电开发有限公司 | Short-term wind power prediction method and system |
| CN116346289A (en) * | 2023-05-30 | 2023-06-27 | 泰山学院 | Data processing method for computer network center |
| CN116600132A (en) * | 2023-07-19 | 2023-08-15 | 华洋通信科技股份有限公司 | Coal mine video data self-adaptive compression method |
| CN116609720A (en) * | 2023-07-19 | 2023-08-18 | 深圳市北汉科技有限公司 | Data-driven-based intelligent error compensation method and system for desk-top multimeter |
| CN116865269A (en) * | 2023-09-01 | 2023-10-10 | 山东泰开电力电子有限公司 | Wind turbine generator system high harmonic compensation method and system |
| CN116913057A (en) * | 2023-09-12 | 2023-10-20 | 西安中创博远网络科技有限公司 | Livestock-raising abnormal early warning system based on thing networking |
-
2023
- 2023-11-27 CN CN202311585327.5A patent/CN117318729A/en active Pending
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20090064914A (en) * | 2007-12-17 | 2009-06-22 | 한국전자통신연구원 | Fine-granular scalability coding/decoding method and apparatus |
| CN114170334A (en) * | 2021-12-08 | 2022-03-11 | 北京欧铼德微电子技术有限公司 | Compensation data compression method and device, electronic equipment and storage medium |
| CN114579050A (en) * | 2022-02-14 | 2022-06-03 | 阿里巴巴（中国）有限公司 | Method and device for processing compressed data |
| CN115017252A (en) * | 2022-08-08 | 2022-09-06 | 深圳市航盛车云技术有限公司 | Intelligent playback system for vehicle track of mobile phone digital vehicle key |
| CN115935819A (en) * | 2022-12-08 | 2023-04-07 | 国电和风风电开发有限公司 | Short-term wind power prediction method and system |
| CN116346289A (en) * | 2023-05-30 | 2023-06-27 | 泰山学院 | Data processing method for computer network center |
| CN116600132A (en) * | 2023-07-19 | 2023-08-15 | 华洋通信科技股份有限公司 | Coal mine video data self-adaptive compression method |
| CN116609720A (en) * | 2023-07-19 | 2023-08-18 | 深圳市北汉科技有限公司 | Data-driven-based intelligent error compensation method and system for desk-top multimeter |
| CN116865269A (en) * | 2023-09-01 | 2023-10-10 | 山东泰开电力电子有限公司 | Wind turbine generator system high harmonic compensation method and system |
| CN116913057A (en) * | 2023-09-12 | 2023-10-20 | 西安中创博远网络科技有限公司 | Livestock-raising abnormal early warning system based on thing networking |
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