A data processing method and device, electronic equipment and storage medium

Abstract

The application provides a data processing method and device, electronic equipment and storage medium. The method comprises the following steps: firstly, obtaining sampling data of an original signal; secondly, intercepting analysis data from the sampling data and performing detection processing on the analysis data to obtain a sampling data signal frequency; and finally, based on the signal frequency, performing decoding processing on the sampling data to obtain decoded data. The application can solve the problem of poor reading accuracy caused by the mismatch between the current decoding frequency and the original signal frequency after fluctuation, and improve the reading accuracy of the non-contact card reader.

Description

Technical Field

[0001] This application relates to the field of signal processing technology, and in particular to a data processing method, apparatus, electronic device and storage medium. Background Technology

[0002] Radio Frequency Identification (RFID) works by enabling contactless data communication between a reader and a tag to identify the target. RFID has a wide range of applications, including contactless card readers, Near Field Communication (NFC), animal microchips, car anti-theft devices, access control, parking management, production line automation, and material management.

[0003] For contactless card readers using RFID, existing readers typically acquire the raw signal and decode it using a decoding frequency that matches the raw signal to read data. However, during the acquisition of the raw signal, the frequency fluctuates due to factors such as the accuracy of the system clock, the accuracy of the input signal conditioning circuit, and the speed of the analog-to-digital converter (ADC). This mismatch between the current decoding frequency and the fluctuating raw signal frequency leads to poor reading accuracy and ultimately low data reading efficiency. Therefore, existing technologies lack solutions to improve the reading accuracy of contactless card readers. Summary of the Invention

[0004] In view of this, embodiments of this application provide a data processing method, apparatus, electronic device, and storage medium that enable a contactless card reader to match the current decoding frequency with the fluctuating frequency of the original signal when decoding the original signal, thereby improving reading accuracy.

[0005] The technical solution of this application embodiment is implemented as follows:

[0006] In a first aspect, embodiments of this application provide a data processing method, including the following steps:

[0007] Acquire sampled data of the original signal, wherein the capacity of the sampled data is a first data capacity;

[0008] The analysis data of a second data capacity is extracted from the sampled data, and the analysis data is processed to obtain the signal frequency of the sampled data, wherein the second data capacity is smaller than the first data capacity;

[0009] Based on the signal frequency, the sampled data is decoded to obtain the decoded data.

[0010] In one possible implementation, the original signal includes one or more sampled data; when the original signal includes multiple sampled data, the signal frequency of each sampled data is fixed; at least two of the multiple sampled data have different fixed signal frequencies.

[0011] The acquisition of the sampled data of the original signal includes:

[0012] The original signal is sampled at a preset sampling rate to obtain the original sampled data of the analog signal;

[0013] The original sampled data of the analog signal is subjected to analog-to-digital conversion to obtain sampled data of a digital signal.

[0014] In one possible implementation, the step of extracting a second data capacity of analysis data from the sampled data and performing detection processing on the analysis data to obtain the signal frequency of the sampled data includes:

[0015] The analysis data is subjected to a first transformation process to obtain first spectral data corresponding to the analysis data, wherein the resolution of the first transformation process is matched with the length of the analysis data;

[0016] Based on the first spectrum data, the maximum frequency value in the first spectrum data is obtained. According to the maximum frequency value, the main lobe where the maximum frequency value is located is determined, and the width of the main lobe is calculated to obtain the main lobe width.

[0017] The sampled data is subjected to a second transformation process to obtain second spectrum data corresponding to the sampled data, wherein the resolution of the second transformation process is matched with the length of the sampled data, and the accuracy of the second transformation process is greater than the accuracy of the first transformation process;

[0018] Based on the main lobe width, the main lobe range in the second spectral data is determined;

[0019] Obtain the center point of the main lobe and use the center point as the signal frequency of the second spectrum data.

[0020] In one possible implementation, calculating the width of the main lobe to obtain the main lobe width includes:

[0021] Subtracting the first fixed value from the maximum frequency value yields the left boundary of the main lobe, and adding the first fixed value to the maximum frequency value yields the right boundary of the main lobe.

[0022] The width of the main lobe is determined based on the left and right boundaries of the main lobe.

[0023] In one possible implementation, the step of decoding the sampled data based on the signal frequency to obtain the decoded data includes:

[0024] The sampled data is filtered to obtain filtered data;

[0025] Based on the signal frequency, a target square wave is constructed, wherein the period of the target square wave matches the signal frequency;

[0026] The target square wave is convolved with the filtered data to obtain the decoded data.

[0027] In one possible implementation, the step of convolving the target square wave with the sampled signal to obtain the decoded data includes:

[0028] Using the sum of the period and the change in the period as a sliding window, the average value of the sampled signal within the sliding window is calculated, wherein each average value includes a confidence level;

[0029] The average value of the confidence level that is greater than the confidence threshold is used as the decoded data.

[0030] In one possible implementation, the method further includes:

[0031] As the sliding window slides, the change is updated based on the difference between the second time and the first time. The second time represents the time difference between the sliding window passing through the third transition edge of the sampling signal and the sliding window passing through the second transition edge of the sampling signal. The first time represents the time difference between the sliding window passing through the second transition edge of the sampling signal and the sliding window passing through the first transition edge of the sampling signal. The first transition edge occurs earlier than the second transition edge occurs, and the second transition edge occurs earlier than the third transition edge occurs.

[0032] Secondly, embodiments of this application also provide a data processing apparatus, the apparatus comprising:

[0033] A sampling module is used to acquire sampled data of the original signal, wherein the capacity of the sampled data is a first data capacity;

[0034] The detection module is used to extract analysis data of a second data capacity from the sampled data and perform detection processing on the analysis data to obtain the signal frequency of the sampled data, wherein the second data capacity is smaller than the first data capacity.

[0035] The decoding module is used to decode the sampled data based on the signal frequency to obtain the decoded data.

[0036] Thirdly, embodiments of this application also provide an electronic device, including: a processor, a storage medium, and a bus, wherein the storage medium stores machine-readable instructions executable by the processor, and when the electronic device is running, the processor communicates with the storage medium via the bus, and the processor executes the machine-readable instructions to perform the steps of the data processing method described in any one of the first aspects.

[0037] Fourthly, embodiments of this application also provide a computer-readable storage medium storing a computer program, which, when executed by a processor, performs the steps of the data processing method described in any one of the first aspects.

[0038] The embodiments of this application have the following beneficial effects:

[0039] By acquiring sampled data of the original signal during the data acquisition stage to obtain sampled data of a first data capacity, and extracting analysis data of a second data capacity from the sampled data, and then detecting the analysis data, the signal frequency of the sampled data can be quickly detected with a lower data volume, thus obtaining the true signal frequency of the sampled data. In the data decoding stage, the obtained true signal frequency is used to decode the sampled data, so that the decoding process can match the true signal frequency, thereby improving the reading accuracy of the contactless card reader. Attached Figure Description

[0040] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0041] Figure 1 This is a flowchart illustrating steps S101-S103 provided in the embodiments of this application;

[0042] Figure 2 This is a flowchart illustrating steps S201-S205 provided in the embodiments of this application;

[0043] Figure 3 This is a flowchart illustrating steps S301-S303 provided in the embodiments of this application;

[0044] Figure 4 This is a flowchart illustrating steps S401-S402 provided in the embodiments of this application;

[0045] Figure 5 This is a schematic diagram of the structure of the data processing apparatus provided in the embodiments of this application;

[0046] Figure 6 This is a schematic diagram of the composition structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0047] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. It should be understood that the accompanying drawings in this application are for illustrative and descriptive purposes only and are not intended to limit the scope of protection of this application. Furthermore, it should be understood that the schematic drawings are not drawn to scale. The flowcharts used in this application illustrate operations implemented according to some embodiments of this application. It should be understood that the operations in the flowcharts may not be implemented in sequence, and steps without logical contextual relationships may be reversed or implemented simultaneously. In addition, those skilled in the art, guided by the content of this application, may add one or more other operations to the flowcharts, or remove one or more operations from the flowcharts.

[0048] In the following description, references are made to “some embodiments,” which describe a subset of all possible embodiments. However, it is understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict.

[0049] Furthermore, the described embodiments are merely some, not all, of the embodiments of this application. The components of the embodiments of this application described and illustrated herein can typically be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of the application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0050] In the following description, the terms "first, second, third" are used merely to distinguish similar objects and do not represent a specific ordering of objects. It is understood that "first, second, third" may be interchanged in a specific order or sequence where permitted, so that the embodiments of this application described herein can be implemented in an order other than that illustrated or described herein.

[0051] It should be noted that the term "comprising" will be used in the embodiments of this application to indicate the presence of the features declared thereafter, but does not exclude the addition of other features.

[0052] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application and is not intended to limit this application.

[0053] See Figure 1 , Figure 1 This is a flowchart illustrating steps S101-S103 of the data processing method provided in this application embodiment, which will be combined with... Figure 1 Steps S101-S103 shown will be explained.

[0054] Step S101: Obtain the sampling data of the original signal, wherein the capacity of the sampling data is the first data capacity;

[0055] Step S102: Extract analysis data of a second data capacity from the sampled data, and perform detection processing on the analysis data to obtain the signal frequency of the sampled data, wherein the second data capacity is smaller than the first data capacity;

[0056] Step S103: Based on the signal frequency, decode the sampled data to obtain the decoded data.

[0057] The above data processing method, in the data acquisition stage, collects sampled data of the original signal to obtain sampled data of a first data capacity, and extracts analysis data of a second data capacity from the sampled data. The analysis data is then tested, thereby enabling rapid detection of the signal frequency of the sampled data with a relatively small amount of data, and obtaining the true signal frequency of the sampled data. In the data decoding stage, the obtained true signal frequency is used to decode the sampled data, so that the decoding process can match the true signal frequency, thereby improving the reading accuracy of the contactless card reader.

[0058] The exemplary steps described above in the embodiments of this application will be explained below.

[0059] In step S101, sampling data of the original signal is acquired, wherein the capacity of the sampling data is the first data capacity.

[0060] Here, the contactless card reader samples the original signal through an ADC to obtain sampled data of a first data capacity. The first data capacity can be preset. In this embodiment, the first data capacity is 16K, where 1K equals 1024B.

[0061] In step 102, analysis data of a second data capacity is extracted from the sampled data, and the analysis data is processed to obtain the signal frequency of the sampled data, wherein the second data capacity is less than the first data capacity.

[0062] In some embodiments, after acquiring the sampled data, it is necessary to detect the signal frequency of the sampled data, where the signal frequency represents the true signal frequency of the sampled data. To improve analysis efficiency, a continuous segment of analytical data is extracted from the sampled data, and the signal frequency of this analytical data is used to represent the signal frequency of the sampled data.

[0063] As an example, 1K of analysis data is extracted from 16K of sampled data. The signal frequency of the 1K analysis data is detected to obtain the signal frequency of the analysis data, and the signal frequency of the analysis data is used as the signal frequency of the sampled data.

[0064] The above method extracts analysis data from the sampled data and performs detection on the analysis data, thereby enabling rapid detection of the signal frequency of the sampled data with a relatively small amount of data, and obtaining the true signal frequency of the sampled data.

[0065] In step S103, the sampled data is decoded based on the signal frequency to obtain the decoded data.

[0066] Here, after obtaining the true signal frequency of the sampled data, the sampled data can be decoded. Since the signal frequency is calculated after the ADC sampling, it is not affected by the accuracy of the system clock, the accuracy of the input signal conditioning circuit, or the sampling rate of the ADC. For this signal frequency, the sampled data is decoded using a decoding method that matches the signal frequency.

[0067] In some embodiments, the original signal includes one or more sampled data; when the original signal includes multiple sampled data, the signal frequency of each sampled data is fixed; at least two of the multiple sampled data have different fixed signal frequencies.

[0068] It should be noted that the original signal may include one or more sampled data. A complete target data segment may consist of one sampled data segment or multiple sampled data segments. Each sampled data segment has a fixed signal frequency, which is affected by the accuracy of the system clock, the accuracy of the input signal conditioning circuit, and the ADC sampling rate. It can be any value within the fluctuation range. Furthermore, due to the influence of the accuracy of the system clock, the accuracy of the input signal conditioning circuit, and the ADC sampling rate, the fixed signal frequencies of at least two sampled data segments among the multiple sampled data segments are different.

[0069] In some embodiments, acquiring the sampled data of the original signal includes:

[0070] The original signal is sampled at a preset sampling rate to obtain the original sampled data of the analog signal;

[0071] The original sampled data of the analog signal is subjected to analog-to-digital conversion to obtain sampled data of a digital signal.

[0072] As an example, the original signal is usually an analog signal. For example, when a bus card, access card, or meal card is brought close to the corresponding contactless card reader, the data reading and writing operations are completed through the transmission of radio waves. The original signal is sent in the form of an analog signal. Therefore, it is necessary to convert the type of the original signal into a digital signal so that the digital signal can be detected and processed, and then the signal frequency of the sampled data of the digital signal can be obtained.

[0073] In some embodiments, see Figure 2 , Figure 2 This is a flowchart illustrating steps S201-S205 provided in the embodiments of this application. The step of extracting analysis data of a second data capacity from the sampled data and performing detection processing on the analysis data to obtain the signal frequency of the sampled data can be achieved through steps S201-S205. Each step will be described in detail below.

[0074] In step S201, the analysis data undergoes a first conversion process to obtain first spectral data corresponding to the analysis data, wherein the resolution of the first conversion process matches the length of the analysis data.

[0075] In some embodiments, the analysis data extracted from the sampled data needs to undergo a first conversion process to be converted into first spectrum data in order to calculate the maximum frequency value of the first spectrum data. In order to improve the conversion efficiency, the resolution of the first conversion process is matched with the length of the analysis data.

[0076] As an example, a Fourier transform (FFT) is performed on the analysis data of length 1K. Here, an FFT of length 1K is used. Since the length of the analysis data is 1K, it is meaningless to use an FFT of too high a length. After the Fourier transform, the first spectrum data is obtained.

[0077] The above method transforms the analysis data to obtain the first spectrum data, which provides a basis for subsequently calculating the maximum frequency of the first spectrum data.

[0078] In step S202, based on the first spectrum data, the maximum frequency value in the first spectrum data is obtained, the main lobe where the maximum frequency value is located is determined according to the maximum frequency value, and the width of the main lobe is calculated to obtain the main lobe width.

[0079] Here, after obtaining the first spectrum data, the maximum frequency value of the first spectrum data can be obtained directly based on the first spectrum data, and the main lobe where the maximum frequency value is located can be determined. After determining the main lobe, it is also necessary to calculate the width of the main lobe that represents the degree of power radiation concentration to obtain the width of the main lobe that represents the degree of power radiation concentration.

[0080] In step S203, the sampled data undergoes a second conversion process to obtain second spectral data corresponding to the sampled data. The resolution of the second conversion process is matched with the length of the sampled data, and the accuracy of the second conversion process is greater than that of the first conversion process.

[0081] In some embodiments, after obtaining the main lobe width, it needs to be applied to the sampled data to determine the main lobe range within the sampled data. Therefore, a second transformation process is required on the sampled data to obtain the second spectral data corresponding to the sampled data. Since the analysis data is extracted from the sampled data, the precision of the second transformation process must be greater than that of the first transformation process, and the resolution of the second transformation process must also match the length of the sampled data.

[0082] As an example, a Fourier transform (FFT) is performed on the sampled data of length 16K. Since the sampled data is 16K long, a 16K FFT is used here. After the Fourier transform, the second spectrum data is obtained.

[0083] The above method transforms the sampled data to obtain the second spectral data of the sampled data, which provides a basis for determining the main lobe range in the second spectral data corresponding to the sampled data by using the main lobe width.

[0084] In step S204, the main lobe range in the second spectral data is determined based on the main lobe width.

[0085] Here, since the main lobe and main lobe width are obtained in the above embodiment, the main lobe range of the second spectrum data can be easily determined in the second spectrum data.

[0086] In step S205, the center point of the main lobe is obtained, and the center point is used as the signal frequency of the second spectrum data.

[0087] Here, the center point of the main lobe, i.e. the inflection point of the main lobe, can be determined based on the range of the main lobe of the second spectrum data. This inflection point represents the signal frequency of the second spectrum data, i.e., the actual signal frequency of the sampled data.

[0088] In some embodiments, calculating the width of the main lobe to obtain the main lobe width includes:

[0089] Subtracting the first fixed value from the maximum frequency value yields the left boundary of the main lobe, and adding the first fixed value to the maximum frequency value yields the right boundary of the main lobe.

[0090] The width of the main lobe is determined based on the left and right boundaries of the main lobe.

[0091] As an example, the left / right boundary of the main lobe is obtained by adding / subtracting a first fixed value from the maximum frequency value Fmax. In this embodiment, the first fixed value is set to 100, and the width of the maximum frequency value Fmax is [Fmax-100, Fmax+100].

[0092] In some embodiments, see Figure 3 , Figure 3 This is a flowchart illustrating steps S301-S303 provided in an embodiment of this application. The step of decoding the sampled data based on the signal frequency to obtain the decoded data can be achieved through steps S301-S303. Each step will be described in detail below.

[0093] In step S301, the sampled data is filtered to obtain filtered data.

[0094] Here, before decoding the sampled data, it is necessary to filter the sampled data to obtain filtered data, ensuring that the filtered data has strict linear phase frequency characteristics while having arbitrary amplitude frequency characteristics. Here, an FIR bandpass filter is used to filter the sampled data.

[0095] In step S302, a target square wave is constructed based on the signal frequency, wherein the period of the target square wave matches the signal frequency.

[0096] Here, a target square wave is constructed using the obtained signal frequency representing the true sampled data, which is then used to decode the filtered data. The period T of the constructed target square wave matches the signal frequency, i.e., T = 1 / f, where f represents the signal frequency, thereby improving the decoding accuracy.

[0097] In step S303, the target square wave is convolved with the filtered data to obtain the decoded data.

[0098] Here, the target square wave is convolved with the filtered data, and a sliding window is used to slide across the filtered data. Classification and regression operations are performed on the data within each window. The classification operation yields different confidence levels, and the regression operation returns values ​​above the confidence level, ultimately resulting in the decoded data.

[0099] The above method constructs a square wave with a period matching the signal frequency by obtaining the actual signal frequency of the sampled data, and then convolves the square wave with the filtered data to adaptively decode the filtered data, thereby improving the decoding accuracy.

[0100] In some embodiments, see Figure 4 , Figure 4 This is a flowchart illustrating steps S401-S402 provided in an embodiment of this application. The step of convolving the target square wave with the sampled signal to obtain the decoded data can be achieved through steps S401-S402, which will be explained in conjunction with each step.

[0101] In step S401, the average value of the sampled signal within the sliding window is calculated using the sum of the period and the change in the period as a sliding window, wherein each average value includes a confidence level;

[0102] As an example, the sliding window is set to T+Δ, where Δ is the change. When the window slides, the average of the data within the window is calculated. If the average is greater than 0.5, the confidence level is set to 1; if it is less than 0.5, the confidence level is set to 0.

[0103] In step S402, the average value of the confidence level greater than the confidence level threshold is used as the decoded data.

[0104] As an example, the confidence threshold here can be flexibly set. In this embodiment, the average value with a confidence level of 1 is returned as the decoded data.

[0105] In some embodiments, the method further includes:

[0106] As the sliding window slides, the change is updated based on the difference between the second time and the first time. The second time represents the time difference between the sliding window passing through the third transition edge of the sampling signal and the sliding window passing through the second transition edge of the sampling signal. The first time represents the time difference between the sliding window passing through the second transition edge of the sampling signal and the sliding window passing through the first transition edge of the sampling signal. The first transition edge occurs earlier than the second transition edge occurs, and the second transition edge occurs earlier than the third transition edge occurs.

[0107] As an example, we need to update Δ in the sliding window based on the transition edges of the convolutional data. Specifically, when the sliding window passes through the sampled signal, it will pass through the transition edges of the sampled signal. At time t1, the sliding window passes through the transition edge y1. At time t2, the sliding window passes through the transition edge y2. At time t3, the sliding window passes through the transition edge y3. Therefore, at time t3, we need to update Δ, where Δ = (t3-t2) - (t2-t1).

[0108] In summary, the embodiments of this application have the following beneficial effects:

[0109] By acquiring sampled data of the original signal during the data acquisition stage to obtain sampled data of a first data capacity, and extracting analysis data of a second data capacity from the sampled data, and then detecting the analysis data, the signal frequency of the sampled data can be quickly detected with a lower data volume, thus obtaining the true signal frequency of the sampled data. In the data decoding stage, the obtained true signal frequency is used to decode the sampled data, so that the decoding process can match the true signal frequency, thereby improving the reading accuracy of the contactless card reader.

[0110] Based on the same inventive concept, this application also provides a data processing device corresponding to the data processing method in the first embodiment. Since the principle of the device in this application is similar to that of the above-mentioned data processing method, the implementation of the device can refer to the implementation of the method, and the repeated parts will not be described again.

[0111] like Figure 5 As shown, Figure 5 This is a schematic diagram of the structure of the data processing apparatus 500 provided in an embodiment of this application. The data processing apparatus 500 includes:

[0112] The sampling module 501 is used to acquire the sampling data of the original signal, wherein the capacity of the sampling data is a first data capacity;

[0113] The detection module 502 is used to extract analysis data of a second data capacity from the sampled data and perform detection processing on the analysis data to obtain the signal frequency of the sampled data, wherein the second data capacity is less than the first data capacity.

[0114] The decoding module 503 is used to decode the sampled data based on the signal frequency to obtain the decoded data.

[0115] Those skilled in the art should understand that Figure 5 The functions of each unit in the data processing apparatus 500 shown can be understood by referring to the relevant description of the aforementioned data processing method. Figure 5 The functions of each unit in the data processing device 500 shown can be implemented by a program running on a processor or by specific logic circuits.

[0116] In one possible implementation, the sampling module 501 acquires sampled data of the original signal, including:

[0117] The original signal is sampled at a preset sampling rate to obtain the original sampled data of the analog signal;

[0118] The original sampled data of the analog signal is subjected to analog-to-digital conversion to obtain sampled data of a digital signal.

[0119] In one possible implementation, the detection module 502 extracts analysis data of a second data capacity from the sampled data and performs detection processing on the analysis data to obtain the signal frequency of the sampled data, including:

[0120] The analysis data is subjected to a first transformation process to obtain first spectral data corresponding to the analysis data, wherein the resolution of the first transformation process is matched with the length of the analysis data;

[0121] Based on the first spectrum data, the maximum frequency value in the first spectrum data is obtained. According to the maximum frequency value, the main lobe where the maximum frequency value is located is determined, and the width of the main lobe is calculated to obtain the main lobe width.

[0122] The sampled data is subjected to a second transformation process to obtain second spectrum data corresponding to the sampled data, wherein the resolution of the second transformation process is matched with the length of the sampled data, and the accuracy of the second transformation process is greater than the accuracy of the first transformation process;

[0123] Based on the main lobe width, the main lobe range in the second spectral data is determined;

[0124] Obtain the center point of the main lobe and use the center point as the signal frequency of the second spectrum data.

[0125] In one possible implementation, the detection module 502 calculates the width of the main lobe to obtain the main lobe width, including:

[0126] Subtracting the first fixed value from the maximum frequency value yields the left boundary of the main lobe, and adding the first fixed value to the maximum frequency value yields the right boundary of the main lobe.

[0127] The width of the main lobe is determined based on the left and right boundaries of the main lobe.

[0128] In one possible implementation, the decoding module 503 performs decoding processing on the sampled data based on the signal frequency to obtain the decoded data, including:

[0129] The sampled data is filtered to obtain filtered data;

[0130] Based on the signal frequency, a target square wave is constructed, wherein the period of the target square wave matches the signal frequency;

[0131] The target square wave is convolved with the filtered data to obtain the decoded data.

[0132] In one possible implementation, the decoding module 503 convolves the target square wave with the sampled signal to obtain the decoded data, including:

[0133] Using the sum of the period and the change in the period as a sliding window, the average value of the sampled signal within the sliding window is calculated, wherein each average value includes a confidence level;

[0134] The data with a confidence level greater than the average confidence level are used as the decoded data.

[0135] In one possible implementation, the decoding module 503 further includes:

[0136] As the sliding window slides, the change is updated based on the difference between the second time and the first time. The second time represents the time difference between the sliding window passing through the third transition edge of the sampling signal and the sliding window passing through the second transition edge of the sampling signal. The first time represents the time difference between the sliding window passing through the second transition edge of the sampling signal and the sliding window passing through the first transition edge of the sampling signal. The first transition edge occurs earlier than the second transition edge occurs, and the second transition edge occurs earlier than the third transition edge occurs.

[0137] The aforementioned data processing device, during the data acquisition stage, collects sampled data of the original signal to obtain sampled data of a first data capacity, and extracts analysis data of a second data capacity from the sampled data. The analysis data is then tested, thereby enabling rapid detection of the signal frequency of the sampled data with a relatively small amount of data, thus obtaining the true signal frequency of the sampled data. During the data decoding stage, the obtained true signal frequency is used to decode the sampled data, ensuring that the decoding process matches the true signal frequency, thereby improving the reading accuracy of the contactless card reader.

[0138] like Figure 6 As shown, Figure 6 This is a schematic diagram of the composition structure of the electronic device 600 provided in the embodiments of this application. The electronic device 600 includes:

[0139] The device 600 includes a processor 601, a storage medium 602, and a bus 603. The storage medium 602 stores machine-readable instructions that can be executed by the processor 601. When the electronic device 600 is running, the processor 601 communicates with the storage medium 602 via the bus 603. The processor 601 executes the machine-readable instructions to perform the steps of the data processing method described in the embodiments of this application.

[0140] In practical applications, the various components in the electronic device 600 are coupled together via a bus 603. It is understood that the bus 603 is used to achieve communication between these components. In addition to a data bus, the bus 603 also includes a power bus, a control bus, and a status signal bus. However, for clarity, in... Figure 6 The general designated all buses as Bus 603.

[0141] The aforementioned electronic device acquires sampled data of the original signal during the data acquisition phase, obtains sampled data of a first data capacity, extracts analysis data of a second data capacity from the sampled data, and detects the analysis data. This allows for rapid detection of the signal frequency of the sampled data with a relatively small amount of data, thus obtaining the true signal frequency of the sampled data. During the data decoding phase, the obtained true signal frequency is used to decode the sampled data, ensuring that the decoding process matches the true signal frequency, thereby improving the reading accuracy of the contactless card reader.

[0142] This application also provides a computer-readable storage medium storing executable instructions that, when executed by at least one processor 601, implement the data processing method described in this application.

[0143] In some embodiments, the storage medium may be a magnetic random access memory (FRAM), a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), a flash memory, a magnetic surface memory, an optical disc, or a compact disc read-only memory (CD-ROM), etc.; or it may be a device that includes one or any combination of the above-mentioned memories.

[0144] In some embodiments, executable instructions may take the form of a program, software, software module, script, or code, written in any form of programming language (including compiled or interpreted languages, or declarative or procedural languages), and may be deployed in any form, including as a standalone program or as a module, component, subroutine, or other unit suitable for use in a computing environment.

[0145] As an example, executable instructions may, but do not necessarily, correspond to files in the file system. They may be stored as part of a file that holds other programs or data, for example, in one or more scripts in a HyperText Markup Language (HTML) document, in a single file dedicated to the program in question, or in multiple collaborating files (e.g., a file that stores one or more modules, subroutines, or code sections).

[0146] As an example, executable instructions can be deployed to execute on a single computing device, or on multiple computing devices located in one location, or on multiple computing devices distributed across multiple locations and interconnected via a communication network.

[0147] The aforementioned computer-readable storage medium, through the data acquisition stage, acquires sampled data of the original signal to obtain sampled data of a first data capacity, extracts analysis data of a second data capacity from the sampled data, and detects the analysis data, thereby enabling rapid detection of the signal frequency of the sampled data with a relatively small amount of data, and obtaining the true signal frequency of the sampled data; in the data decoding stage, it uses the obtained true signal frequency to decode the sampled data, so that the decoding process can match the true signal frequency, thereby improving the reading accuracy of the contactless card reader.

[0148] In the several embodiments provided in this application, it should be understood that the disclosed methods and electronic devices can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods, such as: multiple units or components may be combined, or integrated into another system, or some features may be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the various components shown or discussed may be through some interfaces, and the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0149] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0150] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0151] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a processor-executable, non-volatile, computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, a platform server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, ROM, RAM, magnetic disks, or optical disks.

[0152] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A data processing method, characterized by, The method comprises the following steps: obtaining sample data of an original signal, wherein the capacity of the sample data is a first data capacity; cutting analysis data of a second data capacity from the sample data, and performing detection processing on the analysis data to obtain a signal frequency of the sample data, wherein the second data capacity is less than the first data capacity; based on the signal frequency, performing decoding processing on the sample data to obtain decoded data; the decoding processing on the sample data based on the signal frequency to obtain decoded data comprises: performing filtering processing on the sample data to obtain filtered data; based on the signal frequency, constructing a target square wave, wherein the period of the target square wave matches the signal frequency; performing convolution processing on the target square wave and the filtered data to obtain the decoded data.

2. The method of claim 1, wherein, The original signal comprises one or more sample data; when the original signal comprises multiple sample data, the signal frequency of each sample data is fixed; the fixed signal frequencies of at least two sample data in the multiple sample data are different; the obtaining of the sample data of the original signal comprises: sampling the original signal at a preset sampling rate to obtain original sample data of an analog signal; performing analog-digital conversion processing on the original sample data of the analog signal to obtain sample data of a digital signal.

3. The method of claim 1, wherein, the cutting of analysis data of a second data capacity from the sample data and the performing of detection processing on the analysis data to obtain a signal frequency of the sample data comprises: performing first conversion processing on the analysis data to obtain first frequency spectrum data corresponding to the analysis data, wherein the resolution capability of the first conversion processing matches the length of the analysis data; based on the first frequency spectrum data, obtaining a frequency maximum value in the first frequency spectrum data, determining a main lobe where the frequency maximum value is located according to the frequency maximum value, and calculating the width of the main lobe to obtain a main lobe width; performing second conversion processing on the sample data to obtain second frequency spectrum data corresponding to the sample data, wherein the resolution capability of the second conversion processing matches the length of the sample data, and the accuracy of the second conversion processing is higher than that of the first conversion processing; based on the main lobe width, determining a main lobe range of the main lobe in the second frequency spectrum data; obtaining a center point of the main lobe, and taking the center point as a signal frequency of the second frequency spectrum data.

4. The method of claim 3, wherein, the calculation of the width of the main lobe to obtain a main lobe width comprises: subtracting a first fixed value from the frequency maximum value to obtain a left boundary of the main lobe, and adding a first fixed value to the frequency maximum value to obtain a right boundary of the main lobe; determining the main lobe width according to the main lobe left boundary and the main lobe right boundary.

5. The method of claim 1, wherein, the convolution processing on the target square wave and the filtered data to obtain the decoded data comprises: taking the sum of the period and the change amount of the period as a sliding window, and averaging the filtered data in the sliding window, wherein each average value comprises a confidence degree; taking the average value with a confidence degree greater than the confidence degree as decoded data.

6. The method of claim 5, wherein, The method further comprises: When the sliding window slides, the variation is updated according to a difference between a second time and a first time, wherein the second time represents a time difference between the sliding window passing through the third jump edge of the filtered data and the sliding window passing through the second jump edge of the filtered data, the first time represents a time difference between the sliding window passing through the second jump edge of the filtered data and the sliding window passing through the first jump edge of the filtered data, the first jump edge is generated earlier than the second jump edge, and the second jump edge is generated earlier than the third jump edge.

7. A data processing apparatus, characterized by, The device comprises: a sampling module configured to acquire sampling data of an original signal, wherein a capacity of the sampling data is a first data capacity; a detection module configured to intercept analysis data with a second data capacity from the sampling data, and to perform detection processing on the analysis data to obtain a signal frequency of the sampling data, wherein the second data capacity is less than the first data capacity; a decoding module configured to perform decoding processing on the sampling data based on the signal frequency to obtain decoded data, wherein the decoding processing comprises: performing filtering processing on the sampling data to obtain filtered data; constructing a target square wave based on the signal frequency, wherein a period of the target square wave matches the signal frequency; and performing convolution processing on the target square wave and the filtered data to obtain the decoded data.

8. An electronic device, comprising: comprise: a processor, a storage medium, and a bus, the storage medium storing machine-readable instructions executable by the processor, when the electronic device is running, the processor and the storage medium communicate through the bus, and the processor executes the machine-readable instructions to perform the steps of the data processing method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, and the computer program is executed by the processor to perform the steps of the data processing method according to any one of claims 1 to 6.

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