CN107333289B - Coal mine rescue robot environment information self-derivation wavelet data compression and reconstruction method - Google Patents
Coal mine rescue robot environment information self-derivation wavelet data compression and reconstruction method Download PDFInfo
- Publication number
- CN107333289B CN107333289B CN201710601913.2A CN201710601913A CN107333289B CN 107333289 B CN107333289 B CN 107333289B CN 201710601913 A CN201710601913 A CN 201710601913A CN 107333289 B CN107333289 B CN 107333289B
- Authority
- CN
- China
- Prior art keywords
- wavelet
- coal mine
- sequence
- environment information
- rescue robot
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W24/00—Supervisory, monitoring or testing arrangements
- H04W24/04—Arrangements for maintaining operational condition
-
- H—ELECTRICITY
- H03—ELECTRONIC CIRCUITRY
- H03M—CODING; DECODING; CODE CONVERSION IN GENERAL
- H03M7/00—Conversion of a code where information is represented by a given sequence or number of digits to a code where the same, similar or subset of information is represented by a different sequence or number of digits
- H03M7/30—Compression; Expansion; Suppression of unnecessary data, e.g. redundancy reduction
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L1/00—Arrangements for detecting or preventing errors in the information received
- H04L1/0001—Systems modifying transmission characteristics according to link quality, e.g. power backoff
- H04L1/0014—Systems modifying transmission characteristics according to link quality, e.g. power backoff by adapting the source coding
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W28/00—Network traffic management; Network resource management
- H04W28/02—Traffic management, e.g. flow control or congestion control
- H04W28/0231—Traffic management, e.g. flow control or congestion control based on communication conditions
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W28/00—Network traffic management; Network resource management
- H04W28/02—Traffic management, e.g. flow control or congestion control
- H04W28/06—Optimizing the usage of the radio link, e.g. header compression, information sizing, discarding information
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/90—Services for handling of emergency or hazardous situations, e.g. earthquake and tsunami warning systems [ETWS]
Abstract
The invention discloses a coal mine rescue robot environment information self-derivation wavelet data compression and reconstruction method, which comprises the following steps: acquiring an environmental information sequence of the current position of an underground coal mine rescue robot; secondly, determining the self-derived grade of the environment information sequence; thirdly, judging whether the self-derived grade exceeds a derived grade threshold value; fourthly, data compression of the environment information sequence; fifthly, transmission of the coded and compressed data packet; sixthly, reconstructing data of the environment information sequence; and seventhly, continuously displaying the underground environment information sequence. According to the invention, the signal intensity is acquired in real time through the communication equipment, the wavelet decomposition level is self-derived, the elasticity of information transmission is improved, the coding pretreatment is realized through the multi-scale orthogonal transformation, the Huffman coding efficiency is improved, and the capability of compressing the acquired data information to adapt to the network environment is greatly improved.
Description
Technical Field
The invention belongs to the technical field of coal mine environment acquisition and transmission, and particularly relates to a coal mine rescue robot environment information self-derivation wavelet data compression and reconstruction method.
Background
The communication environment paralysis caused by the underground accident is limited by building an emergency wireless communication network environment by means of the robot technology, and the environment information data is safely, stably and quickly transmitted to a rescue command center under the limited communication condition, so that the environment information data becomes an important guarantee for smoothly developing the rescue work. At present, environmental information data transmission research under good communication network conditions is more, and a data compression reconstruction algorithm is more mature. The environmental information data transmission technology applied to coal mine rescue work under complex geological conditions is less researched, so that a coal mine rescue robot environmental information self-derivation wavelet data compression and reconstruction method is lacking at present, and multiple environmental information data self-adaptive communication transmission conditions are realized on the basis of limited communication channel transmission characteristic research, namely, in a limited change environment, environmental information data are compressed at different depths, the adaptive capacity of data transmission in different communication network environments is improved, the data transmission is guaranteed to be transmitted while a good lossless transmission characteristic is kept, and an important means is provided for improving the data compression capacity.
Disclosure of Invention
The invention aims to solve the technical problem of providing a coal mine rescue robot environment information self-derivation wavelet data compression and reconstruction method aiming at the defects in the prior art, signal strength and self-derivation wavelet decomposition level are acquired in real time through communication equipment, the elasticity of information transmission is improved, coding preprocessing is realized through multi-scale orthogonal transformation, the Huffman coding efficiency is improved, the capability of acquiring data information compression and adapting to a network environment is greatly improved, and the method is convenient to popularize and use.
In order to solve the technical problems, the invention adopts the technical scheme that: the coal mine rescue robot environment information self-derived wavelet data compression and reconstruction method is characterized by comprising the following steps:
the method comprises the following steps of firstly, obtaining an environmental information sequence of the current position of the coal mine rescue robot: acquiring an environmental information sequence f (x) of the current position of the coal mine rescue robot by using the coal mine rescue robot;
the coal mine rescue robot is provided with an environment detector for acquiring an underground roadway environment sequence, a laser detector for detecting underground roadway obstacles and a wireless communication device which is communicated with an upper computer and used for acquiring underground communication signal intensity, and a signal output end of the environment detector and a signal output end of the laser detector and the coal mine rescue robot are both connected with each otherThe input end of the central processing unit of the robot is connected, the output end of the central processing unit of the coal mine rescue robot is connected with a walking mechanism for controlling the coal mine rescue robot to move forward or backward, the data acquired by the environment detector and the laser detector is an environment information sequence f (x) of the current position of the coal mine rescue robot,
wherein the content of the first and second substances,
is the k-th environmental information function in the environmental information sequence f (x), aJ,kAs a function of the kth context information
K is the number of the environment information function;
step two, determining the self-derived grade of the environment information sequence: according to the formula
Determining a self-derived ranking γ of the sequence of environmental information, wherein EγThe communication attenuation signal strength acquired by the wireless communication equipment in real time, E is the inherent communication signal strength of the wireless communication equipment, and η is the communication signal strength attenuation ratio;
step three, judging whether the self-derived grade exceeds a derived grade threshold value: setting a derivative grade threshold Th, and when gamma is larger than or equal to Th, indicating that the wireless communication equipment cannot normally communicate with an upper computer and a wireless communication network is unavailable, and carrying out network repair on a place with serious communication signal intensity loss by a central processing unit of the coal mine rescue robot; when gamma is less than Th, executing step four;
step four, compressing the data of the environment information sequence, wherein the process is as follows:
step 401, determining wavelet decomposition level J of the environment information sequence: determining wavelet decomposition series J according to a formula J, wherein gamma is less than Th;
step 402, multi-scale wavelet packet decomposition of the environmental information sequence: order environment information sequence
The central processing unit of the coal mine rescue robot carries out multi-scale wavelet packet decomposition on the environmental information sequence f (x) to obtain
Wherein n is 2l or 2l +1, and l is a non-negative integer,
is a wavelet decomposition scale space with a level number of J and
⊕ are the orthogonal operators, and the operation,
in order to be orthogonal space for the low-frequency sequence,
is composed of
Orthogonal complement space of, low-frequency sequence orthogonal space
All elements in (1) and the orthogonal complement space
Are orthogonal to each other and any of them,
n is 2 for the nth transform coefficient of the s subband of the J-th order wavelet packet decompositionJ，ψnFor wavelet basis functions, wavelet basis functions ψnIs of the two-dimensional transformation formula
hsFor wavelet basis function psinWith a two-dimensional orthogonal transformation function psi2l(x) Low pass filter of gsFor wavelet basis function psinOrthogonal transformation function psi with another two dimensions2l+1(x) High pass filter of (g)s＝(-1)sh1-s；
Step 403, performing multi-scale orthogonal wavelet packet transformation on the environment information sequence and obtaining a low-frequency sequence coefficient matrix and a high-frequency sequence coefficient matrix: firstly, the central processing unit of the coal mine rescue robot will
Carrying out multi-scale orthogonal wavelet packet transformation to obtain
Wherein m is a subband shift number,
is the 2l transform coefficient of the m sub-band of the J-1 level orthogonal wavelet packet transform and
is the 2l +1 th transform coefficient of the m-th sub-band of the J-1-th level orthogonal wavelet packet transform and
<·,·>representing inner product operation, representing convolution operation, dsSet of coefficient detail sequences, H, for s subbands2lLow pass filters for the wavelet basis function of the current level and the wavelet basis function of the next level, G2l+1A high pass filter for the wavelet basis function of the current level and the wavelet basis function of the next level; then, a low frequency sequence coefficient matrix d is obtained2lAnd a high frequency sequence coefficient matrix d2l+1Wherein the low frequency sequence coefficient matrix d2lIncluding low-frequency sequence coefficients of all sub-bands in each level of the orthogonal wavelet packet transform, i.e.
dJ ,2lA high-frequency sequence coefficient matrix d for the set of low-frequency sequence coefficients of all sub-bands in the J-th orthogonal wavelet packet transform2l+1Including high-frequency sequence coefficients of all sub-bands in each stage of the orthogonal wavelet packet transform, i.e.
dJ,2l+1The high-frequency sequence coefficients of all sub-bands in the J-th level orthogonal wavelet packet transformation are collected;
step 404, low frequency sequence coefficient matrix d2lAnd a high frequency sequence coefficient matrix d2l+1The coding of (2): low-frequency sequence coefficient matrix d of central processing unit of coal mine rescue robot by adopting Huffman coding method2lAnd a high frequency sequence coefficient matrix d2l+1Carrying out coding compression to obtain a coding compression data packet;
and step five, transmission of the coded compressed data packet: transmitting the coding compression data packet to an upper computer by adopting wireless communication equipment through a channel;
step six, reconstructing data of the environment information sequence, wherein the process is as follows:
step 601, huffman decoding: the upper computer sends the received coding compression data packet to a Huffman decoder for data expansion to obtain a decoding data stream, wherein the decoding data stream comprises a decoding low-frequency sequence coefficient matrix
And decoding a high frequency sequence coefficient matrix
Step 602, matching of reconstruction series: host computer identification decoding low-frequency sequence coefficient matrix d'2lAnd decoding a high frequency sequence coefficient matrix d'2l+1The number of decoding coefficient detail sequences J ', J ' is the matching reconstruction series, and the matching reconstruction series J ' is equal to the wavelet decomposition series J;
step 603, wavelet packet reconstruction of the decoded data stream: headFirstly, according to the formula
Calculating the nth transform coefficient of the s subband of the J' th stage
Then, according to the formula
Reconstructing the environment information sequence to obtain an environment information reconstruction sequence f (x)';
and seventhly, continuously displaying the underground environment information sequence: and the upper computer displays the acquired environmental information reconstruction sequence f (x), the central processing unit of the coal mine rescue robot drives the travelling mechanism to move forward or backward, the coal mine rescue robot acquires the environmental information sequence of the current position, the steps from one to six are repeated, the self-derived wavelet data compression and reconstruction of the environmental information of the coal mine rescue robot are realized, and the upper computer continuously displays the environmental information sequence of each current position of the coal mine rescue robot.
The coal mine rescue robot environment information self-derivation wavelet data compression and reconstruction method is characterized by comprising the following steps: the environment detector comprises an environment sensor; in the first step, the wireless communication equipment is a WIFI wireless communication module.
The coal mine rescue robot environment information self-derivation wavelet data compression and reconstruction method is characterized by comprising the following steps: the derivative grade threshold Th is 6 or 7.
The coal mine rescue robot environment information self-derivation wavelet data compression and reconstruction method is characterized by comprising the following steps: and in the third step, the central processing unit of the coal mine rescue robot carries out network repair on the place with serious communication signal intensity loss by controlling the coal mine rescue robot to place a repeater in the underground roadway.
The coal mine rescue robot environment information self-derivation wavelet data compression and reconstruction method is characterized by comprising the following steps: the wavelet basis function psinIs a Haar wavelet basis function or DbN wavelet basis function, where N takes 4 or 8.
Compared with the prior art, the invention has the following advantages:
1. the invention adopts wireless communication equipment to collect the signal intensity of the underground communication channel in real time, determines the wavelet decomposition level number of the environment information sequence according to the signal intensity, and the self-derived wavelet decomposition level, and a multi-scale wavelet decomposition algorithm under the self-derived characteristic needs to be continuously adapted to the transmission characteristic of the wireless communication channel, thereby improving the elasticity of information transmission and being convenient for popularization and use.
2. The method adopts multi-scale wavelet packet decomposition and multi-scale orthogonal wavelet packet transformation to compress the coal mine underground complex environment information acquisition signals, and eliminates partial redundant information in the acquired environment information sequence, so that the signals are safely and reliably transmitted in a limited and variable wireless communication channel, and the primary compression of data is realized; the once compressed data after the self-derived wavelet data are decomposed is compressed secondarily by adopting the Huffman coding, so that the coding efficiency is improved, the coding complexity is reduced, and the method is reliable and stable and has a good using effect.
3. The method has simple steps, and the self-adaptive performance of the data compression coding method in the complex channel is correspondingly improved by combining the thought of the self-derived wavelet data compression algorithm according to the change rule of the wireless communication transmission characteristic in the complex environment under the coal mine, so that the optimal state of the rescue communication network and the effectiveness of data transmission are ensured, the rescue data detected by equipment is not lost as much as possible in the data transmission process, and the capability of compressing the acquired data information and adapting to the network environment is greatly improved.
In conclusion, the invention acquires the signal intensity in real time through the communication equipment, self-derives the wavelet decomposition level, improves the elasticity of information transmission, realizes the coding pretreatment through the multi-scale orthogonal transformation, improves the Huffman coding efficiency, greatly improves the capability of compressing the acquired data information to adapt to the network environment, and is convenient for popularization and use.
The technical solution of the present invention is further described in detail by the accompanying drawings and embodiments.
Drawings
Fig. 1 is a schematic block circuit diagram of a data transmission apparatus employed in the present invention.
FIG. 2 is a block diagram of a method for compressing and reconstructing data according to the present invention.
FIG. 3 is a waveform diagram of a gas concentration information sampling sequence according to the present invention.
Fig. 4 is a waveform diagram of a gas concentration information sampling sequence of the level 1 wavelet decomposition in fig. 3.
Fig. 5 is a waveform diagram of a wavelet-reconstructed gas concentration information sampling sequence of fig. 4.
Fig. 6 is a waveform diagram of a gas concentration information sampling sequence of the 2-level wavelet decomposition in fig. 3.
Fig. 7 is a waveform diagram of a wavelet-reconstructed gas concentration information sampling sequence of fig. 6.
Fig. 8 is a waveform diagram of a gas concentration information sampling sequence of the 3-level wavelet decomposition in fig. 3.
Fig. 9 is a waveform diagram of a wavelet-reconstructed gas concentration information sampling sequence of fig. 8.
Fig. 10 is a waveform diagram of a gas concentration information sampling sequence of the 4-level wavelet decomposition in fig. 3.
Fig. 11 is a waveform diagram of a wavelet-reconstructed gas concentration information sampling sequence of fig. 10.
Fig. 12 is a waveform diagram of a gas concentration information sampling sequence of the 5-level wavelet decomposition in fig. 3.
Fig. 13 is a waveform diagram of a wavelet-reconstructed gas concentration information sampling sequence of fig. 12.
Fig. 14 is a waveform diagram of a gas concentration information sampling sequence of the 6-level wavelet decomposition in fig. 3.
Fig. 15 is a waveform diagram of a wavelet-reconstructed gas concentration information sampling sequence of fig. 14.
FIG. 16 is a waveform diagram of a gas concentration information sampling sequence derived from a multi-level wavelet decomposition according to the present invention.
Fig. 17 is a waveform diagram of a wavelet-reconstructed gas concentration information sampling sequence of fig. 16.
Description of reference numerals:
1-an environment detector; 2-laser detector;
3-a central processing unit of the coal mine rescue robot; 4-a wireless communication device;
5, an upper computer; 6, a traveling mechanism.
Detailed Description
As shown in fig. 1 and 2, the coal mine rescue robot environment information self-derived wavelet data compression and reconstruction method of the invention comprises the following steps:
the method comprises the following steps of firstly, obtaining an environmental information sequence of the current position of the underground coal mine rescue robot: acquiring an environmental information sequence f (x) of the current position of the coal mine rescue robot by using the coal mine rescue robot;
the coal mine rescue robot is provided with an environment detector 1 for acquiring an underground roadway environment sequence, a laser detector 2 for detecting underground roadway obstacles and a wireless communication device 4 which is communicated with an upper computer 5 and used for acquiring underground communication signal intensity, the signal output end of the environment detector 1 and the signal output end of the laser detector 2 are both connected with the input end of a central processing unit 3 of the coal mine rescue robot, the output end of the central processing unit 3 of the coal mine rescue robot is connected with a walking mechanism 6 for controlling the coal mine rescue robot to move forwards or backwards, the data acquired by the environment detector 1 and the laser detector 2 is an environment information sequence f (x) of the current position of the coal mine rescue robot,
wherein the content of the first and second substances,
is the k-th environmental information function in the environmental information sequence f (x), aJ,kAs a function of the kth context information
K is the number of the environment information function;
it should be noted that the environment detector 1 is arranged to detect underground environment parameters in real time, determine the environment of the underground trapped people, and make effective rescue measures for the underground trapped people, the laser detector 2 is arranged to provide a barrier avoiding module for the coal mine rescue robot to walk, so that the coal mine rescue robot is prevented from advancing to a position where the coal mine rescue robot cannot exceed, and the coal mine rescue robot is ensured to continuously and effectively advance, the wireless communication device 4 is arranged to provide a data communication module for the coal mine rescue robot, and the inconvenience of the coal mine rescue robot caused by the overlong lead due to the use of wired communication is avoided, the upper computer 5 can watch the underground environment information collected by the coal mine rescue robot in real time, and an effective guiding function is provided for rescuing the trapped people.
In this embodiment, in the first step, the environment detector 1 includes an environment sensor; in the first step, the wireless communication device 4 is a WIFI wireless communication module.
It should be noted that, the coal mine rescue robot carries out an underground rescue task, needs to enter an underground complex environment to detect whether trapped people exist or not, and simultaneously detects the surrounding environment of the trapped people, so the environment sensor includes a gas sensor, a pressure sensor, and a temperature and humidity sensor, wherein the content of underground gas directly threatens the life safety of the trapped people, the gas sensor is preferably adopted as the environment sensor in this embodiment, and fig. 3 is a sampling waveform diagram of an environment information sequence f (x) acquired by the gas sensor in this embodiment.
Step two, determining the self-derived grade of the environment information sequence: according to the formula
Determining a self-derived ranking γ of the sequence of environmental information, wherein EγThe communication attenuation signal strength acquired in real time for the wireless communication device 4, E is the inherent communication signal strength of the wireless communication device 4, and η is the communication signal strength attenuation ratio;
it should be noted that, the wireless communication device 4 is provided to collect the signal strength of the underground communication channel in real time, the wavelet decomposition level number and the self-derived wavelet decomposition level of the environment information sequence are determined according to the signal strength, and the multi-scale wavelet decomposition algorithm under the self-derived characteristic needs to continuously adapt to the transmission characteristic of the wireless communication channel, so as to improve the flexibility of information transmission.
Step three, judging whether the self-derived grade exceeds a derived grade threshold value: setting a derivative grade threshold Th, and when gamma is larger than or equal to Th, indicating that the wireless communication equipment 4 cannot normally communicate with the upper computer 5 and a wireless communication network is unavailable, and carrying out network repair on a place with serious communication signal intensity loss by the central processing unit 3 of the coal mine rescue robot; when gamma is less than Th, executing step four;
in this embodiment, the derivative level threshold Th is 6 or 7.
It should be noted that, when the signal intensity of the downhole communication channel is weak, the amount of transmitted data is small, the number of wavelet decomposition levels is too many, which easily causes data damage and is not easy to recover, in the preferred embodiment, the derivative level threshold Th is 6, that is, the derivative level γ of the environmental information sequence is 6, so that the communication signal intensity attenuation ratio η is 0.6, that is, when the communication attenuation signal intensity acquired by the wireless communication device 4 in real time is lower than 40% of the inherent communication signal intensity of the wireless communication device 4, it indicates that the wireless communication device 4 cannot normally communicate with the upper computer 5, the wireless communication network is unavailable, and the central processor 3 of the coal mine rescue robot performs network repair on a place with serious communication signal intensity loss.
In this embodiment, in step three, the central processing unit 3 of the coal mine rescue robot performs network repair on a place with serious communication signal strength loss by controlling the coal mine rescue robot to place a repeater in an underground roadway.
The repeaters are arranged in the underground roadway according to the communication strength of the wireless communication device 4 and the upper computer 5, so that the underground communication continuity is kept in the underground environment where the coal mine rescue robot can surmount.
Step four, compressing the data of the environment information sequence, wherein the process is as follows:
step 401, determining wavelet decomposition level J of the environment information sequence: determining wavelet decomposition series J according to a formula J, wherein gamma is less than Th;
in actual use, the wireless communication device 4 is adopted to detect the signal intensity of the underground communication channel at the current position of the coal mine rescue robot in real time, and the wavelet decomposition level J of the environment information sequence is determined according to a formula gamma-INT (10 η).
Step 402, multi-scale wavelet packet decomposition of the environmental information sequence: order environmentInformation sequence
The central processing unit 3 of the coal mine rescue robot carries out multi-scale wavelet packet decomposition on the environment information sequence f (x) to obtain
Wherein n is 2l or 2l +1, and l is a non-negative integer,
is a wavelet decomposition scale space with a level number of J and
⊕ are the orthogonal operators, and the operation,
in order to be orthogonal space for the low-frequency sequence,
is composed of
Orthogonal complement space of, low-frequency sequence orthogonal space
All elements in (1) and the orthogonal complement space
Are orthogonal to each other and any of them,
n is 2 for the nth transform coefficient of the s subband of the J-th order wavelet packet decompositionJ，ψnFor wavelet basis functions, wavelet basis functions ψnIs of the two-dimensional transformation formula
hsFor wavelet basis function psinAnd a two-rulerDegree orthogonal transformation function psi2l(x) Low pass filter of gsFor wavelet basis function psinOrthogonal transformation function psi with another two dimensions2l+1(x) High pass filter of (g)s＝(-1)sh1-s；
In this embodiment, the wavelet basis function ψnIs a Haar wavelet basis function or DbN wavelet basis function, where N is 4 or 8, preferably the embodiment wavelet basis function ψnHaar wavelet basis functions are used.
Step 403, performing multi-scale orthogonal wavelet packet transformation on the environment information sequence and obtaining a low-frequency sequence coefficient matrix and a high-frequency sequence coefficient matrix: firstly, the central processor 3 of the coal mine rescue robot will
Carrying out multi-scale orthogonal wavelet packet transformation to obtain
Wherein m is a subband shift number,
is the 2l transform coefficient of the m sub-band of the J-1 level orthogonal wavelet packet transform and
is the 2l +1 th transform coefficient of the m-th sub-band of the J-1-th level orthogonal wavelet packet transform and
<·,·>representing inner product operation, representing convolution operation, dsSet of coefficient detail sequences, H, for s subbands2lLow pass filters for the wavelet basis function of the current level and the wavelet basis function of the next level, G2l+1A high pass filter for the wavelet basis function of the current level and the wavelet basis function of the next level; then, a low frequency sequence coefficient matrix d is obtained2lAnd a high frequency sequence coefficient matrix d2l+1Wherein the low frequency sequence coefficient matrix d2lIncluding low-frequency sequence coefficients of all sub-bands in each level of the orthogonal wavelet packet transform, i.e.
dJ ,2lA high-frequency sequence coefficient matrix d for the set of low-frequency sequence coefficients of all sub-bands in the J-th orthogonal wavelet packet transform2l+1Including high-frequency sequence coefficients of all sub-bands in each stage of the orthogonal wavelet packet transform, i.e.
dJ,2l+1The high-frequency sequence coefficients of all sub-bands in the J-th level orthogonal wavelet packet transformation are collected; d1,2l+1For the set of high frequency sequence coefficients of all sub-bands in the 1 st order orthogonal wavelet packet transform, d2,2l+1The method is a set of high-frequency sequence coefficients of all sub-bands in the 2 nd-level orthogonal wavelet packet transformation, and the like;
it should be noted that, as shown in fig. 4, fig. 6, fig. 8, fig. 10, fig. 12 and fig. 14, 1-level wavelet decomposition, 2-level wavelet decomposition, 3-level wavelet decomposition, 4-level wavelet decomposition, 5-level wavelet decomposition and 6-level wavelet decomposition are respectively performed on the original environment information sequence f (x) acquired by the gas sensor, and the compression ratio of the actually measured gas data after performing the 1-level wavelet decomposition on the original environment information sequence f (x) acquired by the gas sensor is 66.67%; carrying out 2-level wavelet decomposition on an original environment information sequence f (x) acquired by a gas sensor, and then actually measuring the gas data compression ratio to be 44.12%; carrying out 3-level wavelet decomposition on an original environment information sequence f (x) acquired by a gas sensor, and then actually measuring the gas data compression ratio to be 34.31%; carrying out 4-level wavelet decomposition on an original environment information sequence f (x) acquired by a gas sensor, and then actually measuring the gas data compression ratio to be 29.41%; carrying out 5-level wavelet decomposition on an original environment information sequence f (x) acquired by a gas sensor, and then actually measuring the gas data compression ratio to be 26.47%; and (3) carrying out 6-level wavelet decomposition on an original environment information sequence f (x) acquired by the gas sensor, and then actually measuring the gas data compression ratio to be 25.49%.
As shown in fig. 16, in this embodiment, self-derived multi-level wavelet decomposition is performed on an original environment information sequence f (x) acquired by a gas sensor, fig. 16 is a waveform diagram of a gas concentration information sampling sequence of successively performed 3-level wavelet decomposition, 5-level wavelet decomposition, and 2-level wavelet decomposition, response of data compression is obvious under a complex channel characteristic, a channel changes, data is compressed at a corresponding level, and channel characteristics are reasonably utilized in compression of each portion, in this embodiment, in the complex channel, compression of the original environment information sequence f (x) acquired by the gas sensor reaches a compression ratio of 74%.
Step 404, low frequency sequence coefficient matrix d2lAnd a high frequency sequence coefficient matrix d2l+1The coding of (2): the central processing unit 3 of the coal mine rescue robot adopts a Huffman coding method to carry out low-frequency sequence coefficient matrix d2lAnd a high frequency sequence coefficient matrix d2l+1Carrying out coding compression to obtain a coding compression data packet;
it should be noted that, after the original environment information sequence f (x) collected by the gas sensor is subjected to 1-level wavelet decomposition, 2-level wavelet decomposition, 3-level wavelet decomposition, 4-level wavelet decomposition, 5-level wavelet decomposition and 6-level wavelet decomposition, data redundancy exists through data compression, redundant information is removed from data before entering a channel through Huffman coding, the data complexity is optimized through Huffman coding, the wavelet decomposition data is further compressed, and the compression ratio of actually measured gas data after the 1-level wavelet decomposition data is subjected to Huffman coding is 61.11%; after Huffman coding is carried out on the 2-level wavelet decomposition data, the compression ratio of actually measured gas data is 40.19%; after Huffman coding is carried out on the 3-level wavelet decomposition data, the compression ratio of actually measured gas data is 30.84%; after Huffman coding is carried out on the 4-level wavelet decomposition data, the compression ratio of actually measured gas data is 27.10%; after the 5-level wavelet decomposition data are subjected to Huffman coding, the compression ratio of actually measured gas data is 25.23%; after the Huffman coding is carried out on the 6-level wavelet decomposition data, the compression ratio of actually measured gas data is 24.30%.
In the embodiment, the data obtained by performing huffman coding on the self-derived multi-level wavelet decomposition data is further compressed to achieve a compression ratio of 68%, and meanwhile, the data complexity is reduced, the compression ratio of the data reflects the information amount change before and after the data is compressed, and the data is an important index influencing the size of the data entering a channel, data transmission and the bandwidth of a required transmission channel, and the compression effect is better as the compression ratio is smaller, so that the effectiveness of the huffman coding on the data compression is shown.
And step five, transmission of the coded compressed data packet: transmitting the encoded compressed data packet to an upper computer 5 by adopting a wireless communication device 4 through a channel;
step six, reconstructing data of the environment information sequence, wherein the process is as follows:
step 601, huffman decoding: the upper computer 5 sends the received coding compression data packet to a Huffman decoder for data expansion to obtain a decoding data stream, wherein the decoding data stream comprises a decoding low-frequency sequence coefficient matrix
And decoding a high frequency sequence coefficient matrix
Step 602, matching of reconstruction series: upper computer 5 identifies and decodes low-frequency sequence coefficient matrix d'2lAnd decoding a high frequency sequence coefficient matrix d'2l+1The number of decoding coefficient detail sequences J ', J ' is the matching reconstruction series, and the matching reconstruction series J ' is equal to the wavelet decomposition series J;
step 603, wavelet packet reconstruction of the decoded data stream: firstly, according to the formula
Calculating the nth transform coefficient of the s subband of the J' th stage
Then, according to the formula
Reconstructing the environment information sequence to obtain an environment information reconstruction sequence f (x)'；
It should be noted that, as shown in fig. 5, fig. 7, fig. 9, fig. 11, fig. 13, and fig. 15, data obtained by performing 1-level wavelet decomposition, 2-level wavelet decomposition, 3-level wavelet decomposition, 4-level wavelet decomposition, 5-level wavelet decomposition, and 6-level wavelet decomposition on an original environment information sequence f (x) acquired by a gas sensor are reconstructed, respectively, and the obtained 1-level reconstruction effect error is 1.1036e-15, 2-level reconstruction effect error is 2.1247e-15, 3-level reconstruction effect error is 3.1836e-15, 4-level reconstruction effect error is 3.9905e-15, 5-level reconstruction effect error is 4.8174e-15, and 6-level reconstruction effect error is 5.8849e-15, respectively, data can be transmitted in a narrow wireless communication channel, however, the data compression method with fixed wavelet series will block in a smaller channel, resulting in deviation of data reconstruction waveform. Causing distortion of the information.
And seventhly, continuously displaying the underground environment information sequence: the upper computer 5 displays the obtained environment information reconstruction sequence f (x)', the central processing unit 3 of the coal mine rescue robot drives the walking mechanism 6 to move forward or backward, the coal mine rescue robot obtains the environment information sequence of the current position, the steps from one to six are repeated, the self-derived wavelet data compression and reconstruction of the environment information of the coal mine rescue robot are achieved, and the upper computer 5 continuously displays the environment information sequence of each current position of the underground coal mine rescue robot.
As shown in fig. 17, in this embodiment, data obtained by performing self-derived multi-level wavelet decomposition on an environmental information sequence f (x) of each current position of the coal mine rescue robot, which is acquired by a gas sensor, is reconstructed, the difference between information data obtained after signal reconstruction and original data is few, a self-derived multi-level reconstruction effect error is 2.9580e-15, the self-derived multi-level reconstruction effect error is between levels of 2-3 levels of fixed wavelet decomposition level compression, the larger the compression level is, the smaller the compression ratio is, the larger the reconstruction effect error is, the channel intensity change is accompanied by data compression, and the self-derived data compression realizes stable transmission of a large amount of real-time data under the requirement of limited wireless communication transmission characteristics, reduces channel blockage caused by a large amount of data in a limited channel, and prevents data loss; meanwhile, the self-derived data reconstruction process keeps higher reduction degree, the method well meets the requirements of rescue information acquisition and safe transmission, the distortion is very small, and the method has a better application effect in a complex communication environment.
According to the invention, after the wireless communication system is stably established, the characteristics of the wireless communication channel in the roadway are changed due to the fluctuation of the communication data volume and the change of the external environment, the data is compressed reasonably in a self-adaptive manner according to the acquired strength of the wireless communication channel, on the premise of avoiding losing data details, the complexity of data transmission is reduced through Huffman coding, the elasticity of information transmission is improved, the coding preprocessing is realized through multi-scale orthogonal transformation, the Huffman coding efficiency is improved, and the capability of compressing the acquired data information to adapt to the network environment is greatly improved.
The above description is only a preferred embodiment of the present invention, and is not intended to limit the present invention, and all simple modifications, changes and equivalent structural changes made to the above embodiment according to the technical spirit of the present invention still fall within the protection scope of the technical solution of the present invention.
Claims (4)
1. The coal mine rescue robot environment information self-derived wavelet data compression and reconstruction method is characterized by comprising the following steps:
the method comprises the following steps of firstly, obtaining an environmental information sequence of the current position of the underground coal mine rescue robot: acquiring an environmental information sequence f (x) of the current position of the coal mine rescue robot by using the coal mine rescue robot;
the coal mine rescue robot is provided with an environment detector (1) for collecting an underground roadway environment sequence, a laser detector (2) for detecting underground roadway obstacles and a wireless communication device (4) which is communicated with an upper computer (5) and used for collecting underground communication signal intensity, the signal output end of the environment detector (1) and the signal output end of the laser detector (2) are connected with the input end of a central processing unit (3) of the coal mine rescue robot, the output end of the central processing unit (3) of the coal mine rescue robot is connected with a walking mechanism (6) for controlling the coal mine rescue robot to move forwards or backwards, the data obtained by the environment detector (1) and the laser detector (2) form an environment information sequence f (x) and the environment information sequence f is formed jointlySequence of
Wherein the content of the first and second substances,
is the k-th environmental information function in the environmental information sequence f (x), aJ,kAs a function of the kth context information
K is the number of the environment information function;
step two, determining the self-derived grade of the environment information sequence: according to the formula
Determining a self-derived ranking γ of the sequence of environmental information, wherein EγThe communication attenuation signal strength acquired in real time for the wireless communication equipment (4), E is the inherent communication signal strength of the wireless communication equipment (4), and η is the communication signal strength attenuation ratio;
step three, judging whether the self-derived grade exceeds a derived grade threshold value: setting a derivative grade threshold Th, when gamma is larger than or equal to Th, indicating that the wireless communication equipment (4) cannot normally communicate with the upper computer (5) and the wireless communication network is unavailable, and performing network repair on a place with serious communication signal intensity loss through the central processing unit (3) of the coal mine rescue robot; when gamma is less than Th, executing step four;
a central processing unit (3) of the coal mine rescue robot carries out network restoration on a place with serious communication signal intensity loss by controlling the coal mine rescue robot to place a repeater in an underground roadway;
step four, compressing the data of the environment information sequence, wherein the process is as follows:
step 401, determining wavelet decomposition level J of the environment information sequence: determining wavelet decomposition series J according to a formula J, wherein gamma is less than Th;
step 402, multi-scale wavelet packet decomposition of the environmental information sequence: order environment information sequence
The central processing unit (3) of the coal mine rescue robot carries out multi-scale wavelet packet decomposition on the environment information sequence f (x) to obtain
Wherein n is 2l or 2l +1, and l is a non-negative integer,
is a wavelet decomposition scale space with a level number of J and
in order to be the orthogonal operator, the first and second operators,
in order to be orthogonal space for the low-frequency sequence,
is composed of
Orthogonal complement space of, low-frequency sequence orthogonal space
All elements in (1) and the orthogonal complement space
Are orthogonal to each other and any of them,
n is 2 for the nth transform coefficient of the s subband of the J-th order wavelet packet decompositionJ，ψnFor wavelet basis functions, wavelet basis functions ψnIs of the two-dimensional transformation formula
hsFor wavelet basis function psinWith a two-dimensional orthogonal transformation function psi2l(x) Low pass filter of gsFor wavelet basis function psinOrthogonal transformation function psi with another two dimensions2l+1(x) High pass filter of (g)s＝(-1)sh1-s；
Step 403, performing multi-scale orthogonal wavelet packet transformation on the environment information sequence and obtaining a low-frequency sequence coefficient matrix and a high-frequency sequence coefficient matrix: firstly, a central processing unit (3) of the coal mine rescue robot is used for carrying out rescue operation
Carrying out multi-scale orthogonal wavelet packet transformation to obtain
Wherein m is a subband shift number,
is the 2l transform coefficient of the m sub-band of the J-1 level orthogonal wavelet packet transform and
is the 2l +1 th transform coefficient of the m-th sub-band of the J-1-th level orthogonal wavelet packet transform and
- > represents inner product operation, represents convolution operation, dsCoefficient detail sequence formed for s sub-bandsSet, H2lLow pass filters for the wavelet basis function of the current level and the wavelet basis function of the next level, G2l+1A high pass filter for the wavelet basis function of the current level and the wavelet basis function of the next level; then, a low frequency sequence coefficient matrix d is obtained2lAnd a high frequency sequence coefficient matrix d2l+1Wherein the low frequency sequence coefficient matrix d2lIncluding low-frequency sequence coefficients of all sub-bands in each level of the orthogonal wavelet packet transform, i.e.
dJ,2lA high-frequency sequence coefficient matrix d for the set of low-frequency sequence coefficients of all sub-bands in the J-th orthogonal wavelet packet transform2l+1Including high-frequency sequence coefficients of all sub-bands in each stage of the orthogonal wavelet packet transform, i.e.
dJ,2l+1The high-frequency sequence coefficients of all sub-bands in the J-th level orthogonal wavelet packet transformation are collected;
step 404, low frequency sequence coefficient matrix d2lAnd a high frequency sequence coefficient matrix d2l+1The coding of (2): the central processing unit (3) of the coal mine rescue robot adopts a Huffman coding method to carry out low-frequency sequence coefficient matrix d2lAnd a high frequency sequence coefficient matrix d2l+1Carrying out coding compression to obtain a coding compression data packet;
and step five, transmission of the coded compressed data packet: transmitting the coding compression data packet to an upper computer (5) by adopting a wireless communication device (4) through a channel;
step six, reconstructing data of the environment information sequence, wherein the process is as follows:
step 601, huffman decoding: the upper computer (5) sends the received coding compression data packet to a Huffman decoder for data expansion to obtain a decoding data stream, and the decoding data stream comprises a decoding low-frequency sequence coefficient matrix
And decoding the high frequency sequence systemNumber matrix
Step 602, matching of reconstruction series: the upper computer (5) identifies a decoded low-frequency sequence coefficient matrix d'2lAnd decoding a high frequency sequence coefficient matrix d'2l+1The number of decoding coefficient detail sequences J ', J ' is the matching reconstruction series, and the matching reconstruction series J ' is equal to the wavelet decomposition series J;
step 603, wavelet packet reconstruction of the decoded data stream: firstly, according to the formula
Calculating the nth transform coefficient of the s subband of the J' th stage
Then, according to the formula
Reconstructing the environment information sequence to obtain an environment information reconstruction sequence f (x)';
and seventhly, continuously displaying the underground environment information sequence: the acquired environment information reconstruction sequence f (x)' is displayed through the upper computer (5), the walking mechanism (6) is driven to move forwards or backwards by the central processing unit (3) of the coal mine rescue robot, the coal mine rescue robot acquires the environment information sequence in the current position environment, the steps from one step to the sixth step are repeated, the coal mine rescue robot environment information self-derivation wavelet data compression and reconstruction are achieved, and the environment information sequence of each underground position is continuously displayed through the upper computer (5).
2. The coal mine rescue robot environment information self-derived wavelet data compression and reconstruction method according to claim 1, characterized in that: the environment detector (1) comprises an environment sensor and a pyroelectric sensor; in the first step, the wireless communication equipment (4) is a WIFI wireless communication module.
3. The coal mine rescue robot environment information self-derived wavelet data compression and reconstruction method according to claim 1, characterized in that: the derivative grade threshold Th is 6 or 7.
4. The coal mine rescue robot environment information self-derived wavelet data compression and reconstruction method according to claim 1, characterized in that: the wavelet basis function psinIs a Haar wavelet basis function or DbN wavelet basis function, where N takes 4 or 8.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710601913.2A CN107333289B (en) | 2017-07-21 | 2017-07-21 | Coal mine rescue robot environment information self-derivation wavelet data compression and reconstruction method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710601913.2A CN107333289B (en) | 2017-07-21 | 2017-07-21 | Coal mine rescue robot environment information self-derivation wavelet data compression and reconstruction method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN107333289A CN107333289A (en) | 2017-11-07 |
| CN107333289B true CN107333289B (en) | 2020-04-07 |
Family
ID=60200532
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201710601913.2A Active CN107333289B (en) | 2017-07-21 | 2017-07-21 | Coal mine rescue robot environment information self-derivation wavelet data compression and reconstruction method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN107333289B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111368905B (en) * | 2020-03-02 | 2021-06-22 | 周红 | Signal recovery method and device applied to oil and gas exploration and signal recovery equipment |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102832908A (en) * | 2012-09-20 | 2012-12-19 | 西安科技大学 | Wavelet transform and variable-step-size LMS (least mean square) adaptive filtering based signal denoising method |
| CN104777497A (en) * | 2015-04-24 | 2015-07-15 | 太原理工大学 | Single-antenna Beidou satellite signal wavelet decomposition anti-interference algorithm |
| CN105894476A (en) * | 2016-04-21 | 2016-08-24 | 重庆大学 | Fused SAR image noise reduction processing method based on dictionary learning |
| CN106485764A (en) * | 2016-11-02 | 2017-03-08 | 中国科学技术大学 | The quick exact reconstruction methods of MRI image |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20140376605A1 (en) * | 2013-06-25 | 2014-12-25 | Electronics And Telecommunications Research Institute | Apparatus and method for compressing and decompressing data |
-
2017
- 2017-07-21 CN CN201710601913.2A patent/CN107333289B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102832908A (en) * | 2012-09-20 | 2012-12-19 | 西安科技大学 | Wavelet transform and variable-step-size LMS (least mean square) adaptive filtering based signal denoising method |
| CN104777497A (en) * | 2015-04-24 | 2015-07-15 | 太原理工大学 | Single-antenna Beidou satellite signal wavelet decomposition anti-interference algorithm |
| CN105894476A (en) * | 2016-04-21 | 2016-08-24 | 重庆大学 | Fused SAR image noise reduction processing method based on dictionary learning |
| CN106485764A (en) * | 2016-11-02 | 2017-03-08 | 中国科学技术大学 | The quick exact reconstruction methods of MRI image |
Also Published As
| Publication number | Publication date |
|---|---|
| CN107333289A (en) | 2017-11-07 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US8606351B2 (en) | Compression of electrocardiograph signals | |
| CN103327326B (en) | Based on the SAR image transmission method of compressed sensing and channel self-adapting | |
| CN103398295B (en) | A kind of pipeline magnetic flux leakage signal data compression device and method | |
| CN107045142A (en) | Wavelet field geological data Real Time Compression and High precision reconstruction method based on compressed sensing | |
| CN108093264B (en) | Core image compression, decompressing method and system based on splits' positions perception | |
| CN102176779B (en) | Wireless multimedia sensing network video signal adaptive sampling and spectrum allocation method | |
| CN107333289B (en) | Coal mine rescue robot environment information self-derivation wavelet data compression and reconstruction method | |
| Raja et al. | Performance evaluation on EZW & WDR image compression techniques | |
| Abo-Zahhad et al. | Compression of ECG signals based on DWT and exploiting the correlation between ECG signal samples | |
| CN105118512A (en) | General steganalysis method facing AAC digital audio | |
| Martínez-Enríquez et al. | Lifting transforms on graphs for video coding | |
| Pandian et al. | Adaptive wavelet packet basis selection for zerotree image coding | |
| WO2001097528A2 (en) | Subband coefficient prediction with pattern recognition techniques | |
| CN104244012B (en) | A kind of CT data compression methods | |
| CN113129911A (en) | Audio signal coding compression and transmission method and electronic equipment | |
| Masmoudi et al. | A novel bio-inspired static image compression scheme for noisy data transmission over low-bandwidth channels | |
| CN105027519A (en) | Signal processing method and device | |
| Singh et al. | Neuro-wavelet based efficient image compression using vector quantization | |
| CN103489312A (en) | Traffic flow information collection method based on image compression | |
| Colince et al. | Exploitation of differential pulse code modulation for compression of EMG signals by a combination of DWT and DCT | |
| Kumari et al. | Image quality estimation by entropy and redundancy calculation for various wavelet families | |
| Kumar et al. | ECG signal compression algorithm based on joint-multiresolution analysis (J-MRA) | |
| Panda et al. | Competency assessment of image compression in the lossy and lossless domain | |
| Dubey et al. | A new Set Partitioning in Hierarchical (SPIHT) Algorithm and Analysis with Wavelet Filters | |
| Nagamani et al. | EZW and SPIHT image compression techniques for high resolution satellite imageries |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |