WO2019136658A1

WO2019136658A1 - Infrared data learning method, device, and electronic apparatus

Info

Publication number: WO2019136658A1
Application number: PCT/CN2018/072219
Authority: WO
Inventors: 孙永昌; 沈伟
Original assignee: 鹤壁天海电子信息系统有限公司
Priority date: 2018-01-11
Filing date: 2018-01-11
Publication date: 2019-07-18

Abstract

An infrared data learning method, a device, and an electronic apparatus. The method comprises: acquiring an infrared signal to be learned (S11); calculating carrier cycle data and coding pattern data of the infrared signal (S12); acquiring composite data according to the carrier cycle data and the coding pattern data (S13); and performing compression and error correction on the composite data, and obtaining processed data (S14). The method performs compression on the data after the composite data is acquired, thereby reducing the data size, occupying fewer resources of a single-chip machine, lowering costs and the number of components, and improving the reliability of an apparatus.

Description

Infrared data learning method, device and electronic device

Technical field

The present invention relates to the field of infrared remote control technology, and more particularly to an infrared data learning method, device and electronic device.

Background technique

Infrared remote control technology is a widely used and mature technology, mainly used in electronic products, such as air conditioners, televisions and other electronic products. In order to achieve the purpose of controlling multiple electronic products with one remote control, infrared signal learning method is now used to learn multiple Infrared signal from the remote control.

Specifically, the infrared signal learning method includes: collecting the waveform of the infrared signal by means of the interrupt of the single chip, and recording the length of the high and low levels through the timer to realize the learning of the infrared signal.

Due to the characteristics of the infrared signal, a learning method using direct waveform recording will obtain a large amount of learning data. In particular, when the carrier frequency of the infrared signal to be learned is high, the accuracy of learning is required to be high to achieve a lower learning error, and the learned data will be larger, resulting in failure of conventional devices (such as a single chip microcomputer). Provide enough storage space. When the number of infrared buttons to be learned is large, external large-capacity memory is required, which increases the cost, and increases the number of devices and reduces the reliability of the device.

Summary of the invention

In view of this, the present invention provides an infrared data learning method, apparatus, and electronic device, so as to solve a learning method using direct waveform recording, a large amount of learning data is obtained, and sufficient storage space cannot be provided, and a large-capacity memory is required to be externally improved. Cost, and increasing the number of devices also reduces the reliability of the device.

In order to solve the above technical problems, the present invention adopts the following technical solutions:

An infrared data learning method comprising:

Obtain an infrared signal to be learned;

Calculating carrier period data and pattern data of the infrared signal; wherein the pattern data is baseband data obtained after the infrared signal is subjected to decarrier processing;

Obtaining composite data according to the carrier cycle data and the pattern data;

Compressing and correcting the composite data to obtain processed data.

Preferably, compressing and correcting the composite data to obtain processed data includes:

Selecting one sub-data from the pattern data in the composite data as the first target data;

Converting the first target data into a preset size of data to be processed;

Performing data segmentation on the to-be-processed data to obtain first data and second data; wherein the first data is high-order data, and the second data is low-level data;

And performing bit compression on the to-be-processed data according to the value of the first data and the sign bit of the to-be-processed data to obtain short-type data; wherein, when the data of the first data is zero, discarding First data;

Substituting the short pattern data for the selected sub data;

Returning a step of selecting one sub-data from the pattern data in the composite data as the first target data until each sub-data in the pattern data is replaced with corresponding short pattern data, thereby obtaining First composite data;

Perform error correction and byte compression processing on the first composite data to obtain the processed data.

Preferably, performing error correction and byte compression processing on the first composite data to obtain the processed data, including:

Selecting one data from the first composite data as the second target data;

Counting a first number of occurrences of the second target data in the first composite data, a second occurrence of data that is smaller than a value of the second target data by a first predetermined value, and an occurrence ratio The value of the second target data is greater than the third number of times of the data of the first preset value; wherein, when the first number of times is greater than the second number of times and the third number of times, the second target data The value does not change;

When the second number of times is greater than the first number of times and the third number of times, changing the second target data to data that is smaller than a value of the second target data by a first preset value;

When the third number is greater than the first number of times and the second number of times, changing the second target data to data that is greater than a value of the second target data by a second predetermined value;

Returning a step of selecting one data from the first composite data as the second target data until determining the corresponding processed data of each of the first composite data;

Obtaining second composite data according to the processed data corresponding to each data in the first composite data;

Performing byte compression processing on the second composite data to obtain the processed data.

Preferably, performing byte compression processing on the second composite data to obtain the processed data includes:

Extracting at least one set of repeated data that is continuously repeated in the second composite data;

Processing each set of duplicate data according to a preset format to obtain processed repeated data corresponding to each set of the repeated data; wherein the preset format is according to the repeated data, the second preset value, and the repetition The number of occurrences of data is sequentially arranged;

And the unrepeated data in the second composite data and the processed duplicate data corresponding to each set of the repeated data are arranged and combined according to a positional relationship of the corresponding original data in the second composite data, to obtain the Processed data.

Preferably, calculating carrier period data and pattern data of the infrared signal includes:

Obtaining initial carrier period data of the plurality of infrared signals;

Taking an average value of initial carrier period data of the plurality of infrared signals as the carrier period data;

De-carrier processing the infrared signal to obtain a baseband signal;

The pulse width of the baseband signal waveform is measured to obtain pattern data.

A data learning device comprising:

a signal acquisition module, configured to acquire an infrared signal to be learned;

a calculation module, configured to calculate carrier period data and pattern data of the infrared signal; wherein the pattern data is baseband data obtained after the infrared signal is subjected to carrier processing;

a data combining module, configured to obtain composite data according to the carrier cycle data and the pattern data;

And a processing module, configured to compress and correct the composite data to obtain processed data.

Preferably, the processing module comprises:

Selecting a sub-module, configured to select one sub-data from the pattern data in the composite data as the first target data;

a byte setting submodule, configured to convert the first target data into a preset size of to-be-processed data;

a segmentation sub-module, configured to perform data segmentation on the to-be-processed data to obtain first data and second data; wherein the first data is high-order data, and the second data is low-level data;

a compression submodule, configured to perform bit compression on the to-be-processed data according to a value of the first data and a sign bit of the to-be-processed data to obtain short-type data; wherein, when the data of the first data is When zero, the first data is discarded;

a replacement submodule, configured to replace the short code type data with the selected sub data;

a determining sub-module, configured to determine whether each sub-data in the pattern data is replaced with corresponding short-type data, thereby obtaining first composite data;

The selecting sub-module is further configured to: when the determining sub-module determines that each sub-data in the pattern data is not replaced with corresponding short-type data, and does not obtain the first composite data, from the composite One sub-data is selected as the first target data in the pattern data in the data;

The processing submodule is configured to perform error correction and byte compression processing on the first composite data to obtain the processed data.

Preferably, the processing submodule comprises:

a selecting unit, configured to select one data from the first composite data as the second target data;

a statistical unit, configured to count a first number of times that the second target data appears in the first composite data, a second number of times that the data of the first preset value is smaller than a value of the second target data, and a third number of times that the data of the first predetermined value is greater than the value of the second target data; wherein, when the first number of times is greater than the second number of times and the third number of times, The value of the second target data is not changed;

a first changing unit, configured to change the second target data to be smaller than a value of the second target data when the second number of times is greater than the first number of times and the third number of times Set numerical data;

a second changing unit, configured to change the second target data to be greater than a value of the second target data when the third number is greater than the first number of times and the second number of times Set numerical data;

a determining unit, configured to determine whether to determine corresponding processed data of each data in the first composite data;

The selecting unit is further configured to: when the determining unit determines that the corresponding processed data of each data in the first composite data is not determined, and select one data from the first composite data as the second target data;

a data processing unit, configured to obtain second composite data according to the processed data corresponding to each data in the first composite data;

And a compression unit, configured to perform byte compression processing on the second composite data to obtain the processed data.

Preferably, the compression unit comprises:

Extracting a subunit, configured to extract at least one set of repeated data that is continuously repeated in the second composite data;

a data processing sub-unit, configured to process each set of duplicate data according to a preset format, to obtain processed duplicate data corresponding to each set of the repeated data; wherein the preset format is according to the repeated data, and the second The preset value and the number of occurrences of the repeated data are sequentially arranged;

a combination subunit, configured to perform, according to the positional relationship of the corresponding original data in the second composite data, the unrepeated data in the second composite data and the processed repeated data corresponding to each set of the repeated data. The combination is arranged to obtain the processed data.

Preferably, the calculation module comprises:

a data acquisition submodule, configured to acquire initial carrier period data of the plurality of the infrared signals;

a calculation submodule, configured to use an average value of initial carrier period data of the plurality of the infrared signals as the carrier period data;

a carrier submodule, configured to perform carrier processing on the infrared signal to obtain a baseband signal;

The measurement sub-module is used to measure the pulse width of the baseband signal waveform to obtain the pattern data.

An electronic device comprising: a memory and a processor;

Wherein the memory is used to store a program;

The processor is for calling a program, wherein the program is used to:

Obtain an infrared signal to be learned;

Calculating carrier period data and pattern data of the infrared signal; wherein the pattern data is baseband data obtained after the infrared signal is subjected to de-carrier processing;

Compressing and correcting the composite data to obtain processed data.

Compared with the prior art, the present invention has the following beneficial effects:

The invention provides an infrared data learning method, device and electronic device. After the composite data is obtained in the invention, the data is compressed, thereby reducing the amount of data, consuming less MCU resources, reducing the cost and the number of devices. Improve equipment reliability.

DRAWINGS

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the embodiments or the description of the prior art will be briefly described below. Obviously, the drawings in the following description are only It is a certain embodiment of the present invention, and other drawings can be obtained from those skilled in the art without any creative work.

1 is a flowchart of a method for learning an infrared data according to an embodiment of the present invention;

2 is a flowchart of another method for learning infrared data according to an embodiment of the present invention;

FIG. 3 is a schematic structural diagram of hardware for calculating a carrier period according to an embodiment of the present disclosure;

4 is a schematic structural diagram of hardware for calculating code type data according to an embodiment of the present invention;

FIG. 5 is a flowchart of still another method for learning infrared data according to an embodiment of the present invention;

FIG. 6 is a schematic diagram of a data compression scenario according to an embodiment of the present invention;

FIG. 7 is a schematic structural diagram of hardware for calculating code type data according to an embodiment of the present disclosure;

FIG. 8 is a schematic diagram of a data error correction scenario according to an embodiment of the present invention;

FIG. 9 is a schematic diagram of a hardware structure of a fifth type of calculation code data according to an embodiment of the present invention;

FIG. 10 is a schematic diagram of another scenario of data compression according to an embodiment of the present disclosure;

FIG. 11 is a schematic structural diagram of a data learning apparatus according to an embodiment of the present disclosure;

FIG. 12 is a schematic structural diagram of another data learning apparatus according to an embodiment of the present invention.

Detailed ways

The technical solutions in the embodiments of the present invention are clearly and completely described in the following with reference to the accompanying drawings in the embodiments of the present invention. It is obvious that the described embodiments are only a part of the embodiments of the present invention, but not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative efforts are within the scope of the present invention.

The embodiment of the present invention provides an infrared data learning method. Referring to FIG. 1, the infrared data learning method may include:

S11. Obtain an infrared signal to be learned.

Specifically, the infrared signal is sent by a remote controller or other infrared control device to be learned, and the infrared signal is transmitted in the form of a carrier wave.

S12. Calculate carrier period data and pattern data of the infrared signal.

The pattern data is baseband data obtained by the de-carrier processing of the infrared signal, and the pattern data is data representing length.

Optionally, on the basis of this embodiment, referring to FIG. 2, step S12 may include:

S21. Acquire initial carrier period data of the multiple infrared signals.

S22. The average value of initial carrier period data of the plurality of the infrared signals is used as the carrier period data.

Specifically, step S21 and step S22 are processes for calculating carrier period data of the infrared signal, which are specifically explained according to FIG. 3.

First, the infrared signal enters the edge detector 101, and the edge detector 101 outputs a rising edge pulse of the infrared signal. After the control state machine 102 detects the edge, it delays 3 cycles (the first three cycles are inaccurate), and after removing the first three initial carrier cycle data, the control counter 103 counts the interval of the rising edge pulse. Eight initial carrier period data are successively derived, and eight initial carrier period data are pushed into the shift registers 1041-1048. The data after entering the shift registers 1041-1048 enters the adder 105 for the summation operation, and then divides by 8 using the divider 106 to obtain an average value of the eight initial carrier cycle data to obtain carrier cycle data.

It should be noted that eight shift registers are used in this embodiment, and it is also possible to use other numbers of shift registers.

S23. Perform decarrier processing on the infrared signal to obtain a baseband signal.

S24. Measure the pulse width of the baseband signal waveform to obtain the pattern data.

Specifically, step S23 and step S24 are processes for calculating the pattern data of the infrared signal.

Referring to FIG. 4, after the infrared signal passes through the edge detector 201, only the rising edge pulse is output. After the control state machine 204 detects the rising edge pulse, the counter 203 is used to perform timeout control, and the timeout period is 1.5 times of the calculated carrier cycle data. When the rising edge of the infrared signal arrives, control state machine 204 clears counter 203 and sets the output signal. At this time, the input is considered valid; if the rising edge still does not come after 1.5 carrier period data, it is considered that the current counter 203 is timed out, and the control state machine 204 sets the output signal to 0, and the input is considered invalid. Through this process, the de-carrier processing of the infrared signal is realized, and the modulation and recovery of the baseband signal is released. By measuring the width of the baseband signal waveform, the obtained data is the initial pattern data of the system main clock as the basic unit. If the system main clock frequency is higher, the resulting data bit width is larger. The initial carrier period data is divided by the divider 202 and rounded off to obtain pattern data.

It should be noted that the pattern data with the carrier period as the basic unit obtained by the division operation can initially reduce the pattern data amount and remove the influence of the system main clock frequency on the pattern data amount.

It should be noted that the method for obtaining the pattern data by the above method is because the infrared data is usually a binary on-off key control OOK modulation. When it is high, the carrier is transmitted; when it is low, it is not sent. Use this to judge whether the current data is 0 or 1. Conversely, when going to the carrier, first of all, we must judge whether the rising edge comes. After the rising edge comes, the data is 1. However, how long this 1 lasts is to use the timer to measure. The principle of the measurement is to set the timer time. 1.5 times the carrier cycle data length. When a rising edge occurs in the timing cycle, the clear timer is re-timed. If the timer expires, the carrier is gone, that is, the previous 1 is over. Through this process, we know The length of the infrared data 1, the same reason can know the length of 0. Then, by measuring the length of 1 and the length of 0, the pattern data is obtained by the following divider, rounding, and the like.

S13. Obtain composite data according to the carrier cycle data and the pattern data.

Specifically, combining the carrier cycle data and the pattern data to obtain composite data;

Specifically, referring to FIG. 4, the control state machine 204 can also control the superposition writing of the carrier cycle data and the code length data, and store the FIFO 205 in the FIFO type. In this case, the FIFO contains composite data, and the first two. The byte is the carrier cycle data, and the carrier cycle data is the code length data.

S14. Compress and correct the composite data to obtain processed data.

In this embodiment, after the composite data is obtained, the data is compressed, thereby reducing the amount of data, consuming less MCU resources, reducing the cost, the number of devices, and improving device reliability.

Optionally, on the basis of the foregoing embodiment of the infrared data learning method, referring to FIG. 5, step S14 may include:

S31. Select one sub-data from the pattern data in the composite data as the first target data.

And selecting, according to an arrangement order of each sub-data in the pattern data in the composite data, a sub-data as the first target data;

Specifically, each sub-data in the pattern data may be sorted in order, and at this time, one sub-data with the smallest sequence number may be selected as the first target data.

S32. Convert the first target data into a preset size of data to be processed.

The first target data may be converted into data to be processed whose occupied bit size is a preset size;

Specifically, the preset size may be 14 bits (bits). In this step, the data format of the first target data is unified.

S33. Perform data segmentation on the to-be-processed data to obtain first data and second data.

The first data is high order data, and the second data is low order data.

Specifically, when the data is divided, the data can be divided into two parts, a high 7 bits and a low 7 bits, that is, the first data and the second data, the first data is the upper 7 bits, and the second data is the lower 7 bits.

S34. Perform bit compression on the data to be processed according to the value of the first data and the sign bit of the data to be processed to obtain short code data.

Wherein, when the data of the first data is zero, the first data is discarded. The sign bit can be 0 or 1, indicating that the current baseband waveform is 0 or 1; and the next 14 bits of pending data represent the width of the current baseband waveform (0 or 1).

Specifically, the first data is first read and it is determined whether the first data is all zeros. If the high 7 bits are all zeros, the high 7 bits are discarded, and the sign bit and the lower 7 bits are combined into one byte of data for storage. Among them, the data structure can be as shown in Table 1.

Table 1 compressed data structure

Sign bit

Data 7

Data 6

Data 5

Data 4

Data 3

Data 2

Data 1

If the high 7bit is not zero, the high 7bit is saved, and the sign bit is combined to form 1 byte of data for storage. Where the sign bit is 0 or 1. At this time, the sign bit and the lower 7 bits are also combined into another 1-byte data and stored.

Through this method, the storage space can be minimized under the premise of ensuring the dynamic range of the pattern data. The sign bit is used as the high and low level represented by the current pattern. The short code type data can represent the length data and the polarity data of the infrared long code type data by one byte. Reduce storage space.

S35. Replace the short code type data with the selected sub data.

Specifically, the first target data is updated to short code type data.

Specifically, referring to FIG. 6, in FIG. 6, the data to be processed is divided into four groups, for example, the first group is 0 0000000 1100001, and the second group data is 1 0001110 0101110, and the other two sets of data are referred to FIG. 6.

The upper 7 bits of the first set of data are all 0, then the high 7 bits are directly deleted at this time, and finally the short code type is 01100001, that is, the combination of the sign bit and the 7th bit. This is the first line of data on the right.

For the second set of data, the upper 7 bits are not 0. At this time, the sign bit is combined with the upper 7 bits and the lower 7 bits respectively to obtain 10001110 and 10101110, that is, the second row and the third row of the right data in FIG.

For the third set of data and the fourth set of data, the high 7 is all zero, and the processing of the first set of data can be referred to.

It should be noted that the data to be processed occupies 8 bytes in total, and the short code data corresponding to the obtained data to be processed only occupies 5 bytes. Reduced the amount of data.

And the arrangement order of the short code data corresponding to the data to be processed is consistent with the order of the corresponding data to be processed.

S36. Determine whether each sub-data in the pattern data is replaced with corresponding short pattern data, thereby obtaining first composite data; if each sub-data in the pattern data is replaced with a corresponding short pattern Data, and the first composite data is obtained, step S37 is performed; if each of the sub-data in the pattern data is not replaced with the corresponding short pattern data, and the first composite data is not obtained, the process returns to step S31.

S37. Perform error correction and byte compression processing on the first composite data to obtain the processed data.

In this embodiment, the amount of data is reduced by the method of byte compression, which in turn can occupy less storage resources.

Optionally, on the basis of the previous embodiment, referring to FIG. 7, step S37 may include:

S41. Select one data from the first composite data as the second target data.

In this embodiment, the data may be randomly selected or sequentially selected in order.

S42. Count a first number of times that the second target data appears in the first composite data, a second number of times that the data of the first preset value is smaller than a value of the second target data, and an appearance ratio Determining that the value of the second target data is greater than the third number of times of the data of the first preset value;

Specifically, the first preset value is 1. Specifically, in this embodiment, the second target data is subtracted, and the second target data and the second target data are added to the first composite data. The number of occurrences in .

S43. When the first number of times is greater than the second number of times and the third number of times, the value of the second target data does not change;

Specifically, if the first occurrence of the second target data is the largest, the current data has no error and is directly stored.

S44. When the second number of times is greater than the first number of times and the third number of times, changing the second target data to data that is greater than a value of the second target data by a first preset value;

If the second target data is decremented by the second time, the second target data is the wrong data, the correct data should be the second target data value minus one, and the second target data is changed to the second target data. Subtract the data and store it.

S45. When the third number of times is greater than the first number of times and the second number of times, changing the second target data to data that is greater than a value of the second target data by a second predetermined value;

If the third target data has the third highest number of data, the second target data is erroneous data, the correct data should be the second target data value plus one, and the second target data is changed to the second target data. Add one after the data and store it.

It should be noted that, in this embodiment, the first time that the second target data appears in the first composite data is counted, and the data that is smaller than the second target data is smaller than the first preset value. a second number of times, and a third occurrence of the data of the first predetermined value greater than the value of the second target data to determine whether a data error occurs, because the test finds that during the infrared learning process, due to interference or Learning errors caused by factors such as jitter are not very large, and are mostly in the range of plus or minus one. The value of the specific phase difference can be determined by testing to modify the algorithm for error correction compensation.

S46. Determine whether the corresponding processed data of each data in the first composite data is determined. If it is determined that the corresponding processed data of each data in the first composite data is determined, step S47 is performed. If it is determined that the corresponding processed data of each of the first composite data is not determined, the process returns to step S41.

Specifically, since it is necessary to perform error correction on each data in the first composite data, the step of determining whether to determine the corresponding processed data of each data in the first composite data in this step is added.

S47. Obtain a second composite data according to the processed data corresponding to each data in the first composite data.

Specifically, after correcting each data in the first composite data, the second composite data is obtained.

It should be noted that step S42 and step S47 in this embodiment are for implementing error correction on the first composite data. Avoid the learning error caused by infrared carrier signal jitter during infrared learning, correct the error, and greatly improve the compression performance of subsequent byte compression.

This embodiment will be more clearly understood by those skilled in the art and will now be explained in conjunction with FIG.

In FIG. 8, the original infrared data is received as the first composite data, and the error-corrected infrared data is the second composite data.

Assuming that the number of 149, 150, and 151 occurrences in the first composite data is counted, it can be seen that the number of occurrences of 150 is the highest, so 149 in the first composite data should be 150, and therefore, the 149 in the first composite data is changed. For 150.

S48. Perform byte compression processing on the second composite data to obtain the processed data.

In this embodiment, an error correction step is added, and in addition, it is possible to avoid a data error caused by interference or the like.

Optionally, on the basis of the previous embodiment, referring to FIG. 8, step S48 may include:

S51. Extract at least one set of repeated data that is continuously repeated in the second composite data;

Specifically, the following is an example of what is a continuous repetition. For the data 625362536253, it can be seen that the data 6253 is continuously repeated data, and for the data 625306253, 62563 is not continuously repeated data because there is an additional 0 between the two 6253.

Referring to FIG. 10, in FIG. 10, the infrared data after error correction is the second composite data, and the compressed infrared data is the processed data.

Analysis of the second composite data revealed that 150 62 was repeated 11 times in succession and 150 20 was repeated 19 times in succession. The rest of the data is not continuously repeated.

The 150 62 and 150 20 are duplicate data.

S52. Process each set of duplicate data according to a preset format, and obtain processed duplicate data corresponding to each set of the repeated data.

The preset format is sequentially arranged according to the repeated data, the second preset value, and the number of occurrences of the repeated data. The second preset value can be 128.

As shown in Fig. 10, the group of duplicate data of 150 62 appears 11 times in total, and the processed duplicate data corresponding to 150 62 is 150 62 128 11 .

The remaining duplicate data are sequentially processed in this way to obtain the processed duplicate data corresponding to each set of the repeated data.

It should be noted that the reason why the first preset value is set to 128 is:

Since the binary of 128 is 1000 0000, the translation means that the symbol is 1, but the waveform is 0 in length. This waveform of length 0 does not exist. So once 128 appears, it means it is a keyword, not a pattern data. Use 128 to distinguish between the two, so as not to cause data confusion when restoring data.

S53. Arranging and combining the unrepeated data in the second composite data and the processed duplicate data corresponding to each set of the repeated data according to the positional relationship of the corresponding original data in the second composite data. The processed data.

Specifically, referring to FIG. 10, since the data before 150 62 is not continuously repeated, those data are also arranged according to the previous data arrangement method, and for the repeated data, the processed duplicate data corresponding to the duplicate data is used for replacement, and The processed data is arranged according to the positions created in the second composite data before the data is repeated.

Referring to FIG. 10, in FIG. 10, after the second composite data is compressed, the processed data, the compressed infrared data in FIG. 10 is obtained.

It can be seen that the length of the second composite data is 71 bytes, and the length after compression is 15 bytes, further reducing the amount of data.

It should be noted that after the processed data is obtained, when it is sent to the electronic product controlled by the remote controller, decompression and infrared modulation are required.

Specifically, decompression and compression are inverse operations, and whether there is 128 in the processed data, if yes, the original data is obtained according to the preset format, and then the decompressed data can be obtained.

After decompressing the data, bit decompression is performed by separating the symbol data from the pattern data, that is, converting 8 bits into the format of the previous 14 bit+ sign bit before modulation. Specifically, the device FIFO first reads the decompressed data, including carrier frequency data and waveform data. The carrier frequency data is sent to the carrier generator to generate the carrier of the corresponding frequency, and then the waveform data is read, and the carrier edge is synchronized to ensure that the modulated output waveform is edge-aligned, which is beneficial to reduce the modulation error. The synchronized infrared waveform data is multiplied by a multiplier to implement binary keying modulation, and the modulated infrared data is output to the infrared emitter. At this point, the infrared modulation is completed.

In this embodiment, byte compression is added, and the amount of data can be further reduced.

Optionally, on the basis of the foregoing embodiment of the infrared data learning method, another embodiment of the present invention provides a data learning device. Referring to FIG. 11, the data learning device may include:

a signal acquisition module 301, configured to acquire an infrared signal to be learned;

The calculation module 302 is configured to calculate carrier period data and pattern data of the infrared signal, where the pattern data is baseband data obtained after the infrared signal is subjected to carrier processing;

The data combination module 303 is configured to obtain composite data according to the carrier cycle data and the pattern data.

The processing module 304 is configured to compress and correct the composite data to obtain processed data.

Further, the calculating module 302 can include:

It should be noted that, in the working process of each module and sub-module in this embodiment, refer to the corresponding description in the foregoing embodiment, and details are not described herein again.

Optionally, on the basis of any of the foregoing embodiments of the data learning device, the processing module 304 may include:

a sub-module 3041, configured to select one sub-data from the pattern data in the composite data as the first target data;

a byte setting sub-module 3042, configured to convert the first target data into a preset size of data to be processed;

a segmentation sub-module 3043, configured to perform data segmentation on the to-be-processed data to obtain first data and second data; wherein the first data is high-order data, and the second data is low-level data;

The compression sub-module 3044 is configured to perform bit compression on the to-be-processed data according to the value of the first data and the sign bit of the to-be-processed data to obtain short-type data; wherein, when the first data is When the data is zero, the first data is discarded;

a replacement submodule 3045, configured to replace the short code type data with the selected sub data;

The determining sub-module 3046 is configured to determine whether each sub-data in the pattern data is replaced with corresponding short-type data, thereby obtaining first composite data;

The selection sub-module 3041 is further configured to: when the determining sub-module 3046 determines that each sub-data in the pattern data is not replaced with corresponding short-type data, and does not obtain the first composite data, Selecting one sub-data from the pattern data in the composite data as the first target data;

The processing sub-module 3047 is configured to perform error correction and byte compression processing on the first composite data to obtain the processed data.

Optionally, on the basis of the corresponding embodiment of FIG. 12, the processing submodule 3047 includes:

It should be noted that, in the working process of each module, sub-module, and unit in this embodiment, refer to the corresponding description in the foregoing embodiment, and details are not described herein again.

Optionally, on the basis of the previous embodiment, the compression unit includes:

It should be noted that, in the working process of each module, sub-module, unit, and sub-unit in this embodiment, refer to the corresponding description in the foregoing embodiment, and details are not described herein again.

Optionally, on the basis of the foregoing embodiments of the infrared data learning method and apparatus, another embodiment of the present invention provides an electronic device, including: a memory and a processor;

Wherein the memory is used to store a program;

The processor is for calling a program, wherein the program is used to:

Obtain an infrared signal to be learned;

Compressing and correcting the composite data to obtain processed data.

Further, the processor is configured to perform compression and error correction on the composite data, and when the processed data is obtained, specifically used to:

Converting the first target data into a preset size of data to be processed;

Substituting the short pattern data for the selected sub data;

Further, the processor is configured to perform error correction and byte compression processing on the first composite data, and when the processed data is obtained, specifically used to:

Selecting one data from the first composite data as the second target data;

Further, the processor is configured to perform byte compression processing on the second composite data, and when the processed data is obtained, specifically used to:

Further, when the processor is used to calculate carrier period data and pattern data of the infrared signal, specifically, the processor is specifically configured to:

Obtaining initial carrier period data of the plurality of infrared signals;

De-carrier processing the infrared signal to obtain a baseband signal;

The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments are obvious to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but the scope of the invention is to be accorded

Claims

An infrared data learning method, comprising:

Obtain an infrared signal to be learned;

Calculating carrier period data and pattern data of the infrared signal; wherein the pattern data is baseband data obtained after the infrared signal is subjected to de-carrier processing;

Obtaining composite data according to the carrier cycle data and the pattern data;

Compressing and correcting the composite data to obtain processed data.
The infrared data learning method according to claim 1, wherein compressing and correcting the composite data to obtain processed data comprises:

Selecting one sub-data from the pattern data in the composite data as the first target data;

Converting the first target data into a preset size of data to be processed;

Performing data segmentation on the to-be-processed data to obtain first data and second data; wherein the first data is high-order data, and the second data is low-level data;

And performing bit compression on the to-be-processed data according to the value of the first data and the sign bit of the to-be-processed data to obtain short-type data; wherein, when the data of the first data is zero, discarding First data;

Substituting the short pattern data for the selected sub data;

Returning a step of selecting one sub-data from the pattern data in the composite data as the first target data until each sub-data in the pattern data is replaced with corresponding short pattern data, thereby obtaining First composite data;

Perform error correction and byte compression processing on the first composite data to obtain the processed data.
The infrared data learning method according to claim 2, wherein the first composite data is subjected to error correction and byte compression processing to obtain the processed data, including:

Selecting one data from the first composite data as the second target data;

Counting a first number of occurrences of the second target data in the first composite data, a second occurrence of data that is smaller than a value of the second target data by a first predetermined value, and an occurrence ratio The value of the second target data is greater than the third number of times of the data of the first preset value; wherein, when the first number of times is greater than the second number of times and the third number of times, the second target data The value does not change;

When the second number of times is greater than the first number of times and the third number of times, changing the second target data to data that is smaller than a value of the second target data by a first preset value;

When the third number is greater than the first number of times and the second number of times, changing the second target data to data that is greater than a value of the second target data by a second predetermined value;

Returning a step of selecting one data from the first composite data as the second target data until determining the corresponding processed data of each of the first composite data;

Obtaining second composite data according to the processed data corresponding to each data in the first composite data;

Performing byte compression processing on the second composite data to obtain the processed data.
The infrared data learning method according to claim 3, wherein performing byte compression processing on the second composite data to obtain the processed data comprises:

Extracting at least one set of repeated data that is continuously repeated in the second composite data;

Processing each set of duplicate data according to a preset format to obtain processed repeated data corresponding to each set of the repeated data; wherein the preset format is according to the repeated data, the second preset value, and the repetition The number of occurrences of data is sequentially arranged;

And the unrepeated data in the second composite data and the processed duplicate data corresponding to each set of the repeated data are arranged and combined according to a positional relationship of the corresponding original data in the second composite data, to obtain the Processed data.
The infrared data learning method according to claim 1, wherein calculating carrier period data and pattern data of the infrared signal comprises:

Obtaining initial carrier period data of the plurality of infrared signals;

Taking an average value of initial carrier period data of the plurality of infrared signals as the carrier period data;

De-carrier processing the infrared signal to obtain a baseband signal;

The pulse width of the baseband signal waveform is measured to obtain pattern data.
A data learning device, comprising:

a signal acquisition module, configured to acquire an infrared signal to be learned;

a calculation module, configured to calculate carrier period data and pattern data of the infrared signal; wherein the pattern data is baseband data obtained after the infrared signal is subjected to carrier processing;

a data combining module, configured to obtain composite data according to the carrier cycle data and the pattern data;

And a processing module, configured to compress and correct the composite data to obtain processed data.
The data learning device according to claim 6, wherein the processing module comprises:

Selecting a sub-module, configured to select one sub-data from the pattern data in the composite data as the first target data;

a byte setting submodule, configured to convert the first target data into a preset size of to-be-processed data;

a segmentation sub-module, configured to perform data segmentation on the to-be-processed data to obtain first data and second data; wherein the first data is high-order data, and the second data is low-level data;

a compression submodule, configured to perform bit compression on the to-be-processed data according to a value of the first data and a sign bit of the to-be-processed data to obtain short-type data; wherein, when the data of the first data is When zero, the first data is discarded;

a replacement submodule, configured to replace the short code type data with the selected sub data;

a determining sub-module, configured to determine whether each sub-data in the pattern data is replaced with corresponding short-type data, thereby obtaining first composite data;

The selecting sub-module is further configured to: when the determining sub-module determines that each sub-data in the pattern data is not replaced with corresponding short-type data, and does not obtain the first composite data, from the composite One sub-data is selected as the first target data in the pattern data in the data;

The processing submodule is configured to perform error correction and byte compression processing on the first composite data to obtain the processed data.
The data learning device according to claim 7, wherein the processing submodule comprises:

a selecting unit, configured to select one data from the first composite data as the second target data;

a statistical unit, configured to count a first number of times that the second target data appears in the first composite data, a second number of times that the data of the first preset value is smaller than a value of the second target data, and a third number of times that the data of the first predetermined value is greater than the value of the second target data; wherein, when the first number of times is greater than the second number of times and the third number of times, The value of the second target data is not changed;

a first changing unit, configured to change the second target data to be smaller than a value of the second target data when the second number of times is greater than the first number of times and the third number of times Set numerical data;

a second changing unit, configured to change the second target data to be greater than a value of the second target data when the third number is greater than the first number of times and the second number of times Set numerical data;

a determining unit, configured to determine whether to determine corresponding processed data of each data in the first composite data;

The selecting unit is further configured to: when the determining unit determines that the corresponding processed data of each data in the first composite data is not determined, and select one data from the first composite data as the second target data;

a data processing unit, configured to obtain second composite data according to the processed data corresponding to each data in the first composite data;

And a compression unit, configured to perform byte compression processing on the second composite data to obtain the processed data.
The data learning device according to claim 8, wherein the compression unit comprises:

Extracting a subunit, configured to extract at least one set of repeated data that is continuously repeated in the second composite data;

a data processing sub-unit, configured to process each set of duplicate data according to a preset format, to obtain processed duplicate data corresponding to each set of the repeated data; wherein the preset format is according to the repeated data, and the second The preset value and the number of occurrences of the repeated data are sequentially arranged;

a combination subunit, configured to perform, according to the positional relationship of the corresponding original data in the second composite data, the unrepeated data in the second composite data and the processed repeated data corresponding to each set of the repeated data. The combination is arranged to obtain the processed data.
The data learning device according to claim 6, wherein the calculation module comprises:

a data acquisition submodule, configured to acquire initial carrier period data of the plurality of the infrared signals;

a calculation submodule, configured to use an average value of initial carrier period data of the plurality of the infrared signals as the carrier period data;

a carrier submodule, configured to perform carrier processing on the infrared signal to obtain a baseband signal;

The measurement sub-module is used to measure the pulse width of the baseband signal waveform to obtain the pattern data.
An electronic device, comprising: a memory and a processor;

Wherein the memory is used to store a program;

The processor is for calling a program, wherein the program is used to:

Obtain an infrared signal to be learned;

Calculating carrier period data and pattern data of the infrared signal; wherein the pattern data is baseband data obtained after the infrared signal is subjected to de-carrier processing;

Obtaining composite data according to the carrier cycle data and the pattern data;

Compressing and correcting the composite data to obtain processed data.