CN106647399A - Control system and method for intelligent English learning machine - Google Patents

Abstract

The invention provides a control system and method for an intelligent English learning machine, and the system comprises a processor which is in wireless connection with a server, an intelligent terminal, a big data module and a power module, and is used for carrying out the preprocessing of input signals of the server, the intelligent terminal and the big data module, and feeding back the signals to the intelligent terminal, wherein the data is stored in the big data module; the server which is used for updating the learning contents of the learning machine at any time through the Internet according to the current teaching contents; the intelligent terminal which is used for understanding the contents; the big data module which is used for storing the knowledge points which usually enable college students to make a mistake and to be liable to be confused; and a power module which is used for providing stable power for the processor. The system enables the English learning machine to meet the learning requirements in a better way, facilitates the repeated learning, and further improves the learning process.

Description

A kind of intelligent English learning machine control system and control method

Technical field

The invention belongs to English study technical field, more particularly to a kind of intelligent English learning machine control system and controlling party Method.

Background technology

English learning machine adopts modern scientific application technology, with reference to the learning characteristic of English language, it is possible to achieve self-service The English language study of formula.English learning machine has abundant function, can be many-sided right from grammer, language, hearing, read-write etc. Learner is trained.Also there is the functions such as dictionary, translation, data check, can be used as the aid of language learning.Right brain King's English learning machine be it is a can allow user within the shortest time rapid adjustment English study state to optimum state, lift English The sophisticated high-tech biophysics English study electronic product of language application level, by vision photoconduction and the work of acoustic resonance With, the brain wave of user is controlled, the right brain potential of acute activation, allow user to open You Nao language learnings area；Adjustment The English study state of user.With reference to the English study course that speed is played, right brain memory is carried out, make full use of what is be activated Brain language learning function；Subconscious right brain English study is carried out by right brain method for learning English, so as to make the user can To complete English study target, English study application level is lifted.

But, existing learning machine cannot realize remote operation, and study content is fixed, it is impossible to adapt to the new content of courses.

The content of the invention

It is an object of the invention to provide a kind of intelligent English learning machine control system and control method, it is intended to solve existing Learning machine cannot realize remote operation, study content is fixed, it is impossible to adapt to the problem of the new content of courses.

The present invention is achieved in that a kind of intelligent English learning machine control system, the intelligent English learning machine control System includes：

Processor, is wirelessly connected, for server, intelligence with server, intelligent terminal, big data module, power module Energy terminal, the input signal of big data module are pre-processed, and signal are fed back into intelligent terminal, and data are in big data module Middle storage；The energy ezpenditure consumption models of processor, the form of embodying is：

E_rx(k)=E_re-elec(k)=kE_elec；

Wherein, E_elecRepresent intelligent terminal energy consumption, ε_{free-space-amp}And ε_two-way-ampRespectively represent free space model and The amplifier energy consumption of multichannel consumption models, d₀It is constant, d is communication node standoff distance, and k is the data bit that send or receive Number, E_tx(k, d) and E_rxEnergy consumption when () represents that processor sends and receives data respectively k；By LEACH energy consumption models Obtain the dump energy of the processor node；

Server, for according to the current content of courses, by the study content of internet renewal learning machine at any time；

Intelligent terminal, for understanding study content；The clustering ensemble method of intelligent terminal includes：From Random Subspace Method K grader of parallel construction is ensureing；If n XML document set D=(d₁,d₂,…,d_n), wherein d_i=(x_i1,x_i2,..., x_in) for document sets i-th document, claim d_iFor i-th data point in n dimension datas space, it has n dimensional feature vectors, x_i1For it Similarity value；Obtain the classification results of K base grader, be provided with K processor, each processor while scanned samples collection, Rearrangement is entered according to similarity size with the corresponding sample of same keyword, k is randomly extracted to the result after rearrangement One new samples collection of subset constructionEach processor just obtains a new sample set, k process Machine obtains new samples collection and constitutes k base grader；Then concurrently in each base grader its point is tried to achieve using K- nearest neighbor algorithms Class result；

Big data module, for often mistake, the confusing knowledge point of appearance to be stored by university student；The data of big data module Compression method includes：In coding, first according to E1_n+1=E1_n+d_n+1Formula calculates E1 values, further according toWithFormula calculates regression criterion, when calculating this two step, is required to Out-of-limit judgement is carried out to result, judge E1 it is whether out-of-limit be to make in order to avoid it exceedes the sensing data bus upper limit Into spilling；Judge residual error it is whether out-of-limit be in order to realize piecewise fitting, to improve fitting precision；When one section of input data Regression criterion all calculated after, constructShown packet, passes through S-Huffman coding methods carry out entropy code to it, then send, when receiving terminal is decoded, first by receive one group of number According to decoding, { d is restored_n,E1_n,DFR₃,DFR₄,…DFR_nPacket shown in formula, then basisFormula calculates and restores all initial data；

Power module, for providing stable power supply for processor.

Further, digital matched filter, the digital matched filtering are provided between the processor and intelligent terminal Device includes：

Each sampled value to being gathered carries out the A/D modular converters of sample quantization；

It is connected with the A/D modular converters, for n sampling point before and after the same chip of gathered data is carried out Open, obtain the odd and even sequence data on I roads and Q roads, the odd and even sequence data on I roads and Q roads to acquisition are carried out The serial/parallel conversion module of output；

Be connected with the serial/parallel conversion module, for receive serial/parallel conversion module output I roads and Q roads it is strange Secondary and even sequence data, is entered by Golay sequence correlators to the odd and even sequence data on the I roads that received and Q roads The matched filtering module of row Data Matching；

It is connected with the matched filtering module, for the I roads that export the matched filtering module and Q roads odd and idol Secondary sequence data reverts to original I road and Q circuit-switched data sequences, and original I road and Q circuit-switched data sequences are exported it is parallel/serial Modular converter；

It is connected with the parallel/serial modular converter, for receiving the original I road and Q roads of the parallel/serial modular converter output Data sequence, original I road and Q circuit-switched datas sequence are carried out it is squared and, and it is squared to original I road and Q circuit-switched data sequences and The squared and module that exported of result；

It is connected with described squared and module, for the original I road of described squared and module output and Q circuit-switched datas The result of the squared sum of sequence carries out peakvalue's checking, realizes the synchronous coherent detection module of main synchronizing sequence；

Many sub- matched filters are provided with the matched filtering module, if carrying out n secondary data samplings, are needed I Each chip samples value on road and Q roads respectively enters 2n sub- matched filter of parallel connection；

Described squared and module is squared to original I road and Q circuit-switched data sequences using look-up method, is entered using exampleization four Rechoning by the abacus adder summation processed, realizes with carry look ahead chain；

Quaternary rechoning by the abacus adder in described squared and module is asynchronous serial rechoning by the abacus adder, using two weights High pearl for 5 and 5 weights are 1 low pearls knot structure, and unit can represent decimal range for 0-15, just be one four The expression scope of the number of system, simultaneously because squared results are 24bit, using example sentence 6 quarternary full adders is replicated Adder unit, six quaternary adder units are cascaded using the method for carry look ahead chain；

When the serial/parallel conversion module separates 2 samplings in front and back that same chip is carried out, each sampled value is entered When row 4bit quantifies, i.e., 4bitI roads and 4bit Q roads are converted into parallel 4bit I roads odd numbered sequences, 4bit I roads even number sequence Row, 4bit Q road odd numbered sequences and 4bit Q roads even order, respectively enteing matched filtering module to four tunnel sequences carries out correlation Computing, and the parallel/serial modular converter of result Jing is converted into the I roads sequence of 12bit and the Q roads sequence of 12bit.

Further, the transmission function of the sub- matched filter is： C_iBe by hierarchical sequence u, v modulation, u is hierarchical Golay sequence u={ 1,1,1,1,1,1, -1 }, v=1,1,1, - 1, -1,1, -1, -1,1,1,1, -1,1, -1,1,1 },-C_16m+n=u_nv_m；

According to The Golay sequence pairs of layering transfer function by improvement, then have：

H(z_u)=[1+z^-8+z^-1(1-z^-8)][1+z^-4+z^-2(1-z^-4)]；

H(z_v)=(1+z^-1)[1-z^-6+z^-8+z^-14]+(1-z^-1)[z^-2-z^-4+z^-10+z^-12]；

The coherent detection module is compared using the correlation of bubbling comparison method, i.e. adjacent moment and higher value is stored in Register A, the position of higher value is stored in register B, constantly updates, and until there is identical value, whether test position differs code length Cycle, if it is, with regard to carrying out one-time detection again, continuous both sides detect and are treated as acquisition success；

The matched filtering module is mainly made up of delay unit and multiplicaton addition unit, and delay unit is realized using d type flip flop, Multiplicaton addition unit is using common multiply-add module；The matched filtering module realizes that to Golay sequence capturings sequence is entered by being input into Matched filter, it is multiply-add to carry out displacement, and result is exported, when there is Golay sequences to pass through matched filter, matched filter Output maximum 256.

Intelligent English learning machine control system and control method that the present invention is provided, by internet and intelligent terminal kimonos Business device connection, server according to the current content of courses, the study content of renewal learning machine at any time so that English teaching machine can be more Good use study requirement；By big data module, normal wrong, the confusing knowledge point of appearance of university student can be stored, just In repeatedly study, learning process is further improved.

Description of the drawings

Fig. 1 is intelligent English learning machine control system architecture schematic diagram provided in an embodiment of the present invention；

In figure：1st, processor；2nd, server；3rd, intelligent terminal；4th, big data module；5th, power module.

Specific embodiment

In order that the objects, technical solutions and advantages of the present invention become more apparent, with reference to embodiments, to the present invention It is further elaborated.It should be appreciated that specific embodiment described herein is not used to only to explain the present invention Limit the present invention.

The application principle of the present invention is explained in detail below in conjunction with the accompanying drawings.

As shown in figure 1, intelligent English learning machine control system provided in an embodiment of the present invention includes：Processor 1, server 2nd, intelligent terminal 3, big data module 4, power module 5.

Processor 1, is wirelessly connected with server 2, intelligent terminal 3, big data module 4, power module 5, for service Device 2, intelligent terminal 3, the input signal of big data module 4 are pre-processed, and signal is fed back into intelligent terminal 3, and data exist Store in big data module 4.The energy ezpenditure consumption models of processor 1, the form of embodying is：

E_rx(k)=E_re-elec(k)=kE_elec；

Wherein, E_elecRepresent intelligent terminal energy consumption, ε_{free-space-amp}And ε_two-way-ampRespectively represent free space model and The amplifier energy consumption of multichannel consumption models, d₀It is constant, d is communication node standoff distance, and k is the data bit that send or receive Number, E_tx(k, d) and E_rxEnergy consumption when () represents that processor sends and receives data respectively k；By LEACH energy consumption models Obtain the dump energy of the processor node；

Server 2, for according to the current content of courses, by the study content of internet renewal learning machine at any time.

Intelligent terminal 3, for understanding study content；The clustering ensemble method of intelligent terminal includes：From stochastic subspace K grader of method parallel construction is ensureing；If n XML document set D=(d₁,d₂,…,d_n), wherein d_i=(x_i1,x_i2,..., x_in) for document sets i-th document, claim d_iFor i-th data point in n dimension datas space, it has n dimensional feature vectors, x_i1For it Similarity value；Obtain the classification results of K base grader, be provided with K processor, each processor while scanned samples collection, Rearrangement is entered according to similarity size with the corresponding sample of same keyword, k is randomly extracted to the result after rearrangement One new samples collection of subset constructionEach processor just obtains a new sample set, k process Machine obtains new samples collection and constitutes k base grader；Then concurrently in each base grader its point is tried to achieve using K- nearest neighbor algorithms Class result.

Big data module 4, for often mistake, the confusing knowledge point of appearance to be stored by university student；The number of big data module 4 Include according to compression method：In coding, first according to E1_n+1=E1_n+d_n+1Formula calculates E1 values, further according toWithFormula calculates regression criterion, calculate this two During step, be required to carry out out-of-limit judgement to result, judge E1 it is whether out-of-limit be in order to avoid it exceedes sensing data bus The upper limit and cause overflow；Judge residual error it is whether out-of-limit be in order to realize piecewise fitting, to improve fitting precision；When one section it is defeated Enter data regression criterion all calculated after, construct { d_n,E1_n,DFR₃,DFR₄,…DFR_nShown in packet, lead to Cross S-Huffman coding methods carries out entropy code to it, then sends, when receiving terminal is decoded, first by receive one group Data are decoded, and restore { d_n,E1_n,DFR₃,DFR₄,…DFR_nPacket shown in formula, then basisFormula calculates and restores all initial data；

Power module 5, for providing stable power supply for processor 1.

Further, digital matched filter, the digital matched filtering are provided between the processor and intelligent terminal Device includes：

Each sampled value to being gathered carries out the A/D modular converters of sample quantization；

It is connected with the A/D modular converters, for n sampling point before and after the same chip of gathered data is carried out Open, obtain the odd and even sequence data on I roads and Q roads, the odd and even sequence data on I roads and Q roads to acquisition are carried out The serial/parallel conversion module of output；

Be connected with the serial/parallel conversion module, for receive serial/parallel conversion module output I roads and Q roads it is strange Secondary and even sequence data, is entered by Golay sequence correlators to the odd and even sequence data on the I roads that received and Q roads The matched filtering module of row Data Matching；

It is connected with the matched filtering module, for the I roads that export the matched filtering module and Q roads odd and idol Secondary sequence data reverts to original I road and Q circuit-switched data sequences, and original I road and Q circuit-switched data sequences are exported it is parallel/serial Modular converter；

It is connected with the parallel/serial modular converter, for receiving the original I road and Q roads of the parallel/serial modular converter output Data sequence, original I road and Q circuit-switched datas sequence are carried out it is squared and, and it is squared to original I road and Q circuit-switched data sequences and The squared and module that exported of result；

It is connected with described squared and module, for the original I road of described squared and module output and Q circuit-switched datas The result of the squared sum of sequence carries out peakvalue's checking, realizes the synchronous coherent detection module of main synchronizing sequence；

Many sub- matched filters are provided with the matched filtering module, if carrying out n secondary data samplings, are needed I Each chip samples value on road and Q roads respectively enters 2n sub- matched filter of parallel connection；

Described squared and module is squared to original I road and Q circuit-switched data sequences using look-up method, is entered using exampleization four Rechoning by the abacus adder summation processed, realizes with carry look ahead chain；

Quaternary rechoning by the abacus adder in described squared and module is asynchronous serial rechoning by the abacus adder, using two weights High pearl for 5 and 5 weights are 1 low pearls knot structure, and unit can represent decimal range for 0-15, just be one four The expression scope of the number of system, simultaneously because squared results are 24bit, using example sentence 6 quarternary full adders is replicated Adder unit, six quaternary adder units are cascaded using the method for carry look ahead chain；

When the serial/parallel conversion module separates 2 samplings in front and back that same chip is carried out, each sampled value is entered When row 4bit quantifies, i.e., 4bitI roads and 4bit Q roads are converted into parallel 4bit I roads odd numbered sequences, 4bit I roads even number sequence Row, 4bit Q road odd numbered sequences and 4bit Q roads even order, respectively enteing matched filtering module to four tunnel sequences carries out correlation Computing, and the parallel/serial modular converter of result Jing is converted into the I roads sequence of 12bit and the Q roads sequence of 12bit.

Further, the transmission function of the sub- matched filter is： C_iBe by hierarchical sequence u, v modulation, u is hierarchical Golay sequence u={ 1,1,1,1,1,1, -1 }, v=1,1,1, - 1, -1,1, -1, -1,1,1,1, -1,1, -1,1,1 }, C_16m+n=u_nv_m；

According to The Golay sequence pairs of layering transfer function by improvement, then have：

H(z_u)=[1+z^-8+z^-1(1-z^-8)][1+z^-4+z^-2(1-z^-4)]；

H(z_v)=(1+z^-1)[1-z^-6+z^-8+z^-14]+(1-z^-1)[z^-2-z^-4+z^-10+z^-12]；

The coherent detection module is compared using the correlation of bubbling comparison method, i.e. adjacent moment and higher value is stored in Register A, the position of higher value is stored in register B, constantly updates, and until there is identical value, whether test position differs code length Cycle, if it is, with regard to carrying out one-time detection again, continuous both sides detect and are treated as acquisition success；

The matched filtering module is mainly made up of delay unit and multiplicaton addition unit, and delay unit is realized using d type flip flop, Multiplicaton addition unit is using common multiply-add module；The matched filtering module realizes that to Golay sequence capturings sequence is entered by being input into Matched filter, it is multiply-add to carry out displacement, and result is exported, when there is Golay sequences to pass through matched filter, matched filter Output maximum 256.

Presently preferred embodiments of the present invention is the foregoing is only, not to limit the present invention, all essences in the present invention Any modification, equivalent and improvement made within god and principle etc., should be included within the scope of the present invention.

Claims (3)

1. a kind of intelligent English learning machine control system, it is characterised in that the intelligent English learning machine control system includes：

Processor, is wirelessly connected with server, intelligent terminal, big data module, power module, for whole to server, intelligence End, the input signal of big data module are pre-processed, and signal are fed back into intelligent terminal, and data are deposited in big data module Storage；The energy ezpenditure consumption models of processor, the form of embodying is：

$E_{tx}(k,d)=E_{tx-elec}\left(k\right)+E_{tx-amp}(k,d)=\left\{\begin{array}{c}{\mathrm{kE}}_{elec}+{\mathrm{k\ϵ}}_{free-space-amp}{d}^2,d\≤d_0\\ {\mathrm{kE}}_{elec}+{\mathrm{k\ϵ}}_{two-way-amp}{d}^2,d\≥d_0\end{array}\right.;$

E_rx(k)=E_re-elec(k)=kE_elec；

Wherein, E_elecRepresent intelligent terminal energy consumption, ε_{free-space-amp}And ε_two-way-ampFree space model and multichannel are represented respectively The amplifier energy consumption of consumption models, d₀It is constant, d is communication node standoff distance, and k is the data bits that send or receive, E_tx(k, d) and E_rxEnergy consumption when () represents that processor sends and receives data respectively k；It is obtained by LEACH energy consumption models The dump energy of the processor node；

Server, for according to the current content of courses, by the study content of internet renewal learning machine at any time；

Intelligent terminal, for understanding study content；The clustering ensemble method of intelligent terminal includes：It is parallel from Random Subspace Method Construct k grader to ensure；If n XML document set D=(d₁,d₂,…,d_n), wherein d_i=(x_i1,x_i2,...,x_in) be I-th document of document sets, claims d_iFor i-th data point in n dimension datas space, it has n dimensional feature vectors, x_i1For the similar of it Angle value；Obtain the classification results of K base grader, be provided with K processor, each processor while scanned samples collection, having The corresponding sample of same keyword enters rearrangement according to similarity size, and k subset structure is randomly extracted to the result after rearrangement Make a new samples collectionEach processor just obtains a new sample set, and k processor is obtained New samples collection constitutes k base grader；Then concurrently in each base grader its classification results is tried to achieve using K- nearest neighbor algorithms；

Big data module, for often mistake, the confusing knowledge point of appearance to be stored by university student；The data compression of big data module Method includes：In coding, first according to E1_n+1=E1_n+d_n+1Formula calculates E1 values, further according to WithFormula calculates regression criterion, when calculating this two step, is required to carry out out-of-limit judgement to result, Judge E1 it is whether out-of-limit be cause in order to avoid it exceedes the sensing data bus upper limit overflow；Judging whether residual error is out-of-limit is In order to realize piecewise fitting, to improve fitting precision；After the regression criterion of one section of input data has all been calculated, constructShown packet, carries out entropy code, so by S-Huffman coding methods to it After send, when receiving terminal is decoded, first receive one group of data are decoded, restore { d_n,E1_n,DFR₃,DFR₄,… DFR_nPacket shown in formula, then basisF_nFormula R Calculate and restore all initial data；

Power module, for providing stable power supply for processor.

2. intelligence English learning machine control system as claimed in claim 1, it is characterised in that the processor and intelligent terminal Between be provided with digital matched filter, the digital matched filter includes：

Each sampled value to being gathered carries out the A/D modular converters of sample quantization；

It is connected with the A/D modular converters, separates for n sampling before and after the same chip of gathered data is carried out, The odd and even sequence data on I roads and Q roads are obtained, the odd and even sequence data on I roads and Q roads to acquisition are exported Serial/parallel conversion module；

Be connected with the serial/parallel conversion module, for receive the I roads of serial/parallel conversion module output and the odd on Q roads and Even sequence data, line number is entered by Golay sequence correlators to the odd and even sequence data on the I roads that received and Q roads According to the matched filtering module of matching；

It is connected with the matched filtering module, for the I roads that export the matched filtering module and Q roads odd and even sequence Column data reverts to original I road and Q circuit-switched data sequences, and to parallel/serial conversion that original I road and Q circuit-switched data sequences are exported Module；

It is connected with the parallel/serial modular converter, for receiving original I road and the Q circuit-switched datas of the parallel/serial modular converter output Sequence, original I road and Q circuit-switched datas sequence are carried out it is squared and, and to original I road and the knot of the squared sum of Q circuit-switched data sequences The squared and module that fruit is exported；

It is connected with described squared and module, for the original I road of described squared and module output and Q circuit-switched data sequences The result of squared sum carries out peakvalue's checking, realizes the synchronous coherent detection module of main synchronizing sequence；

Many sub- matched filters are provided with the matched filtering module, if carrying out n secondary data samplings, are needed I roads and Q Each chip samples value on road respectively enters 2n sub- matched filter of parallel connection；

Described squared and module is squared to original I road and Q circuit-switched data sequences using look-up method, using example quaternary pearl Adder summation is calculated, is realized with carry look ahead chain；

Quaternary rechoning by the abacus adder in described squared and module is asynchronous serial rechoning by the abacus adder, adopts two weights for 5 High pearl and low pearls knot structure that 5 weights are 1, unit can represent decimal range for 0-15, just enter for one four The expression scope of the number of system, simultaneously because squared results are 24bit, using example sentence 6 quarternary full adders of duplication Adder unit, six quaternary adder units are cascaded using the method for carry look ahead chain；

When the serial/parallel conversion module separates 2 samplings in front and back that same chip is carried out, each sampled value is carried out When 4bit quantifies, i.e., 4bitI roads and 4bit Q roads are converted into parallel 4bit I roads odd numbered sequences, 4bit I roads even number sequence Row, 4bit Q road odd numbered sequences and 4bit Q roads even order, respectively enteing matched filtering module to four tunnel sequences carries out correlation Computing, and the parallel/serial modular converter of result Jing is converted into the I roads sequence of 12bit and the Q roads sequence of 12bit.

3. intelligence English learning machine control system as claimed in claim 2, it is characterised in that the biography of the sub- matched filter Delivery function is：C_iBe by hierarchical sequence u, v modulation, u is layering Golay sequences u=1,1,1,1,1,1, -1, -1, v={ 1,1,1, -1, -1,1, -1, -1,1,1,1, -1,1, -1,1,1 }, C_16m+n=u_nv_m；

According to layering Golay sequence pairs transfer function by improvement, then have：

H(z_u)=[1+z^-8+z^-1(1-z^-8)][1+z^-4+z^-2(1-z^-4)]；

H(z_v)=(1+z^-1)[1-z^-6+z^-8+z^-14]+(1-z^-1)[z^-2-z^-4+z^-10+z^-12]；

The coherent detection module is compared using the correlation of bubbling comparison method, i.e. adjacent moment and higher value is stored in deposit Device A, the position of higher value is stored in register B, constantly updates, and until there is identical value, whether test position differs the code length cycle, If it is, with regard to carrying out one-time detection again, continuous both sides detect and are treated as acquisition success；

The matched filtering module is mainly made up of delay unit and multiplicaton addition unit, and delay unit is realized using d type flip flop, multiply-add Unit is using common multiply-add module；The matched filtering module realizes that to Golay sequence capturings sequence enters matching by input Wave filter, it is multiply-add to carry out displacement, and result is exported, when there is Golay sequences to pass through matched filter, matched filter output Maximum 256.