A low-cost HDX characteristic frequency fast measurement method and device

Abstract

The present invention discloses a low-cost HDX characteristic frequency rapid measurement method and device, and relates to the field of wireless communication and radio frequency identification technology. The low-cost HDX characteristic frequency rapid measurement method and device uses the simplest filtering circuit to remove the original carrier signal, and then directly converts the measurement signal into a specific digital signal through a digital shaping circuit, and only uses a low-cost MPU as an operation unit, so that the cost of the entire device is greatly reduced, and the digital signal and the original carrier signal are compared, and a convolutional neural network algorithm is used to analyze the data, remove abnormal sampling data, and automatically correct the effective sampling data, and restore the interfered digital signal to the state before the interference, and then use these corrected data to perform spectrum analysis, thereby improving the accuracy and efficiency of measurement, reducing equipment costs, and being suitable for a variety of application scenarios.

Description

Technical Field

[0001] The invention relates to the technical field of wireless communication and radio frequency identification, and in particular to a low-cost HDX characteristic frequency fast measurement method and device. Background Art

[0002] With the rapid development of IoT technology, various IoT devices are being used more and more widely, including a large number of HDX tags. These tags are widely used in animal husbandry, logistics, warehousing and other fields, and their characteristic frequencies need to be quickly measured to ensure the normal operation of the equipment. With the continuous advancement of science and technology, especially in the fields of electronic technology, signal processing technology and artificial intelligence, it provides technical support and innovative power for the development of low-cost HDX characteristic frequency fast measurement methods.

[0003] HDX uses FSK technology to perform code 0 and code 1 through different frequency outputs. Code 1 requires a frequency of 124.2KHz with a deviation of no more than ±2KHz, and code 0 requires a frequency of 134.2KHz with a deviation of no more than ±1.5KHz. In the production process of electronic tags, these two frequency parameters will be affected by many factors such as antenna materials, winding methods, magnetism, welding process, potting glue, temperature changes, etc., so it is impossible to calculate and solidify this parameter in a simple way. The production process can only be adjusted and improved through continuous process experiments and physical measurements.

[0004] However, the spectrum measurement instruments for special electronic tags are very expensive, costing hundreds of thousands of dollars. In addition, since they are professional equipment, the measurement efficiency is very low and the technical requirements for the measurement personnel are also very high. It is difficult to use them as a routine detection process in the production process.

[0005] Traditional frequency measurement methods usually require expensive equipment and complex technical means, resulting in increased costs. HDX tags often work in complex environments and are greatly affected by external interference, which may affect the accuracy of the measurement results of traditional methods. Summary of the invention

[0006] In view of the deficiencies in the prior art, the present invention provides a low-cost HDX characteristic frequency fast measurement method and device, which solves the problems of the above-mentioned background technology.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: a low-cost HDX characteristic frequency rapid measurement method, characterized in that it includes the following steps: S1. Extracting and retaining the measurement signal sent by the HDX tag through a signal acquisition device; S2. Removing the original carrier signal through a filtering circuit, using a digital shaping circuit to convert the filtered measurement signal into a digital signal, and performing preprocessing to obtain a digital signal data set; S3. Based on a convolutional neural network algorithm, performing data analysis on the digital signal data set to obtain a digital signal anomaly index, and based on the digital signal anomaly index, removing abnormal digital signals, and automatically correcting the effective digital signals; S4. Performing Fourier transform and spectrum analysis on the digital signal after automatic correction to extract frequency features and obtain the characteristic frequency sent by the HDX tag; S5. Outputting the characteristic frequency sent by the HDX tag.

[0008] Furthermore, based on the convolutional neural network algorithm, the specific process of performing data analysis on the digital signal data set is as follows: based on the convolutional neural network algorithm, a digital signal comparison model is established through the comparison relationship between the original carrier signal and the digital signal, and the characteristic data of the digital signal is obtained. The characteristic data of the digital signal includes digital signal time domain data, digital signal frequency domain data, and digital signal phase data; the digital signal time domain data includes digital signal amplitude, digital signal waveform time interval and digital signal period; the digital signal frequency domain data includes digital signal power spectrum density and digital signal spectrum density; the digital signal phase data includes digital signal time delay, digital signal phase difference and digital signal phase offset.

[0009] Furthermore, the specific process of establishing the digital signal comparison model is as follows: obtaining the original carrier signal and the corresponding digital signal data set as a training data set, performing time domain feature extraction and frequency domain feature extraction, marking the original carrier signal and the corresponding digital signal data set, using the original carrier signal and the digital signal as a pair of input and output, using a convolutional neural network supervised learning algorithm as a comparison model, learning the mapping relationship between the original carrier signal and the digital signal, and establishing a digital signal comparison model.

[0010] Furthermore, the specific process of obtaining the digital signal anomaly index is as follows: based on the digital signal time domain data, the original carrier signal is compared with the digital signal in the time domain to obtain the digital signal time domain anomaly index; based on the digital signal frequency domain data, the original carrier signal is compared with the digital signal in the frequency domain to obtain the digital signal frequency domain anomaly index; based on the digital signal phase data, the original carrier signal is compared with the digital signal phase to obtain the digital signal phase anomaly index; weights are assigned to the digital signal time domain anomaly index, the digital signal frequency domain anomaly index and the digital signal phase anomaly index, and comprehensive operations are performed to obtain the digital signal anomaly index.

[0011] Furthermore, the specific formula of the digital signal anomaly index is as follows:

[0012]

[0013] In the formula, γhg represents the digital signal anomaly index, αjk represents the digital signal time domain anomaly index, βbn represents the digital signal frequency domain anomaly index, δte represents the digital signal phase anomaly index, ω1 represents the weight factor of the digital signal time domain anomaly index, ω2 represents the weight factor of the digital signal frequency domain anomaly index, and ω3 represents the weight factor of the digital signal phase anomaly index.

[0014] Furthermore, the specific process of obtaining the digital signal time domain anomaly index is as follows: based on the digital signal time domain data, the digital signal amplitude, digital signal waveform time interval and digital signal period are differentiated and compared with the original carrier signal amplitude, original carrier signal waveform time interval and original carrier signal period, and ratio operations are performed with the allowable deviation value of the digital signal amplitude, the allowable deviation value of the digital signal waveform time interval and the allowable deviation value of the digital signal period, respectively; weights are assigned to the digital signal amplitude, digital signal waveform time interval and digital signal period, and a comprehensive operation is performed with the differentiated ratio to obtain the digital signal time domain anomaly index.

[0015] Furthermore, the specific process of obtaining the digital signal frequency domain anomaly index is as follows: based on the digital signal frequency domain data, weights are assigned to the power spectral density of the digital signal and the spectral density of the digital signal, and the power spectral density of the digital signal and the spectral density of the digital signal are differenced with the power spectral density of the original carrier signal and the spectral density of the original carrier signal, and compared with the allowable deviation value of the power spectral density and the allowable deviation value of the spectral density, respectively, to obtain the digital signal frequency domain anomaly index.

[0016] Furthermore, the specific process of obtaining the digital signal phase anomaly index is as follows: based on the digital signal phase data, the time delay of the digital signal, the phase difference of the digital signal and the phase offset of the digital signal are differentiated and compared with the time delay of the original carrier signal, the phase difference of the original carrier signal and the phase offset of the original carrier signal, and ratio operations are performed with the allowable deviation value of the time delay of the digital signal, the allowable deviation value of the phase difference of the digital signal and the allowable deviation value of the phase offset of the digital signal, and a comprehensive operation is performed to obtain the digital signal phase anomaly index.

[0017] Furthermore, the specific process of removing abnormal digital signals based on the digital signal anomaly index and automatically correcting the valid digital signals is as follows: comparing the digital signal anomaly index with the standard value of the digital signal anomaly index, identifying abnormal data points in the digital signal, and deleting the abnormal data points from the digital signal data set; automatically correcting the valid digital signal through a correction algorithm, and the automatic correction operation includes calibration, adjustment and correction to keep consistent with the standard signal.

[0018] A low-cost HDX characteristic frequency rapid measurement device comprises the following modules: a signal acquisition module, a filtering and conversion module, a data processing module, a frequency calculation module, and a frequency output module; the signal acquisition module is used to extract and retain the measurement signal sent by the HDX tag through the signal acquisition device; the filtering and conversion module is used to remove the original carrier signal through the filtering circuit, use the digital shaping circuit to convert the filtered signal into a digital signal, and perform preprocessing to obtain a digital signal data set; the data processing module is used to perform data analysis on the digital signal data set based on the convolutional neural network algorithm, obtain the digital signal anomaly index, remove the abnormal digital signal based on the digital signal anomaly index, and automatically correct the effective digital signal; the frequency calculation module is used to perform Fourier transform and spectrum analysis on the digital signal after automatic correction to extract the frequency characteristics, and obtain the characteristic frequency sent by the HDX tag; the frequency output module is used to output the characteristic frequency sent by the HDX tag.

[0019] The present invention has the following beneficial effects:

[0020] (1) This is a low-cost HDX characteristic frequency rapid measurement method. By comparing the digital signal with the original carrier signal, a convolutional neural network algorithm is used to analyze the data, remove abnormal sampling data, and automatically correct the valid sampling data, and restore the interfered digital signal to the state before the interference. The corrected data is then used for spectrum analysis, thereby improving the accuracy and efficiency of the measurement, reducing the equipment cost, and being suitable for a variety of application scenarios.

[0021] (2) This low-cost HDX characteristic frequency rapid measurement device, by setting up a signal acquisition module, a filtering and conversion module, a data processing module, a frequency calculation module, and a frequency output module, and only using a low-cost MPU as a computing unit, the cost of the entire device is greatly reduced, the equipment cost is reduced, and the measurement efficiency and reliability are improved.

[0022] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1The figure is a flow chart of a low-cost HDX characteristic frequency rapid measurement method of the present invention.

[0024] Figure 2 This is a structural diagram of a low-cost HDX characteristic frequency rapid measurement device of the present invention. DETAILED DESCRIPTION

[0025] The embodiments of the present application solve the problems of high cost and insufficient precision in the rapid measurement of HDX characteristic frequency by providing a low-cost method and device for rapid measurement of HDX characteristic frequency.

[0026] The overall idea of ​​the problem in the embodiment of this application is as follows:

[0027] The measurement signal sent by the HDX tag is extracted and retained by the signal acquisition device.

[0028] The original carrier signal is removed by a filtering circuit, the filtered measurement signal is converted into a digital signal using a digital shaping circuit, and preprocessing is performed to obtain a digital signal data set.

[0029] Based on the convolutional neural network algorithm, the digital signal data set is analyzed to obtain the digital signal anomaly index. Based on the digital signal anomaly index, the abnormal digital signal is removed and the effective digital signal is automatically corrected.

[0030] Perform Fourier transform and spectrum analysis on the digital signal after automatic correction to extract frequency characteristics, obtain the characteristic frequency sent by the HDX tag, and output the characteristic frequency sent by the HDX tag.

[0031] Example 1

[0032] See also Figure 1 The embodiment of the present invention provides a technical solution: a low-cost HDX characteristic frequency rapid measurement method, characterized in that it includes the following steps: S1. Extracting and retaining the measurement signal sent by the HDX tag through a signal acquisition device; S2. Removing the original carrier signal through a filtering circuit, using a digital shaping circuit to convert the filtered measurement signal into a digital signal, and performing preprocessing to obtain a digital signal data set; S3. Based on a convolutional neural network algorithm, performing data analysis on the digital signal data set to obtain a digital signal anomaly index, and based on the digital signal anomaly index, removing abnormal digital signals, and automatically correcting the effective digital signals; S4. Performing Fourier transform and spectrum analysis on the digital signal after automatic correction to extract frequency features, and obtaining the characteristic frequency sent by the HDX tag; S5. Outputting the characteristic frequency sent by the HDX tag.

[0033] In this embodiment, the frequency features of the signal after automatic correction are extracted by Fourier transform and spectrum analysis. These features may include the amplitude, phase or other parameters of the frequency component. Finally, the extracted characteristic frequency sent by the HDX tag is output for user reference or further processing.

[0034] Specifically, based on the convolutional neural network algorithm, the specific process of data analysis on the digital signal data set is as follows: based on the convolutional neural network algorithm, through the contrast relationship between the original carrier signal and the digital signal, a digital signal comparison model is established to obtain the characteristic data of the digital signal. The characteristic data of the digital signal includes digital signal time domain data, digital signal frequency domain data, and digital signal phase data; the digital signal time domain data includes digital signal amplitude, digital signal waveform time interval and digital signal period; the digital signal frequency domain data includes the power spectrum density of the digital signal and the spectrum density of the digital signal; the digital signal phase data includes the time delay of the digital signal, the phase difference of the digital signal and the phase offset of the digital signal.

[0035] In this implementation scheme, the amplitude of the digital signal represents the amplitude of the signal, which is an important feature of the signal in the time domain; the time interval of the digital signal waveform represents the time interval between adjacent sampling points, which determines the resolution and accuracy of the digital signal in time; the digital signal period represents the time length of a complete cycle in the signal waveform. The power spectrum density of the digital signal represents the energy distribution of the signal in the frequency domain, reflecting the spectral characteristics of the signal; the spectral density of the digital signal represents the energy density of the signal at different frequencies, which is an important indicator of the frequency domain characteristics of the signal. The time delay of the digital signal represents the time delay of the signal propagation, describing the speed and delay of the signal transmission in space or system. The phase difference of the digital signal represents the phase difference between the signals, which is usually used to measure the relative phase of the signal in the field of signal processing and communication; the phase offset of the digital signal represents the offset of the signal phase relative to the reference signal, which is used to describe the change of the signal phase characteristics. There are many models of convolutional neural network algorithms. After a lot of experiments, we finally adopted a one-dimensional convolution algorithm with a depth of 12 layers, a parameter amount of about 6K, and a parameter type compressed to Float16. Actual measurements show that the success rate of the convolutional neural network algorithm can reach more than 98%.

[0036] Specifically, the specific process of establishing a digital signal comparison model is as follows: obtain the original carrier signal and the corresponding digital signal data set as a training data set, perform time domain feature extraction and frequency domain feature extraction, mark the original carrier signal and the corresponding digital signal data set, use the original carrier signal and the digital signal as a pair of input and output, use the convolutional neural network supervised learning algorithm as a comparison model, learn the mapping relationship between the original carrier signal and the digital signal, and establish a digital signal comparison model.

[0037] In this embodiment, the original carrier signal and the digital signal data set are obtained for the data of the training model. The original carrier signal refers to an analog signal, such as a sound signal, an electrical signal, etc., while the digital signal is a digitized version after sampling and quantization. The comparison of these two signals can be used to train the model to automatically perform signal conversion or processing. Time domain feature extraction and frequency domain feature extraction extract features from the original signal. Time domain features can include amplitude, waveform, period, etc., while frequency domain features involve the distribution of the signal in the frequency domain, such as power spectral density, etc. The extraction of these features is usually achieved through signal processing technology and digital signal processing algorithms. The labeled data set labels the original carrier signal and the corresponding digital signal so that the supervised learning algorithm learns the mapping relationship between them. Labeling usually includes pairing the original signal and the digital signal and assigning corresponding labels or categories to them.

[0038] Specifically, the specific process of obtaining the digital signal anomaly index is as follows: based on the digital signal time domain data, the original carrier signal is compared with the digital signal in the time domain to obtain the digital signal time domain anomaly index; based on the digital signal frequency domain data, the original carrier signal is compared with the digital signal in the frequency domain to obtain the digital signal frequency domain anomaly index; based on the digital signal phase data, the phase of the original carrier signal and the digital signal are compared to obtain the digital signal phase anomaly index; weights are assigned to the digital signal time domain anomaly index, the digital signal frequency domain anomaly index and the digital signal phase anomaly index, and comprehensive operations are performed to obtain the digital signal anomaly index.

[0039] In this embodiment, time domain comparison: compare the original carrier signal with the digital signal in the time domain. This can be achieved by comparing their amplitude, waveform and other characteristics. The comparison result can be used to calculate the digital signal time domain anomaly index. Compare the original carrier signal with the digital signal in the frequency domain. This can be achieved by comparing their spectrum distribution, frequency components and other characteristics. The comparison result can be used to calculate the digital signal frequency domain anomaly index. Compare the phase of the original carrier signal with the digital signal. This can be achieved by comparing their phase angle, phase difference and other characteristics. The comparison result can be used to calculate the digital signal phase anomaly index. The time domain anomaly index, frequency domain anomaly index and phase anomaly index with weight distribution are comprehensively calculated.

[0040] Specifically, in addition to processing the digital signal by using an adaptive filter to suppress noise and interference and highlight the abnormal part, and calculating the abnormal index based on the amplitude and frequency information of the abnormal part, the digital signal abnormal index can also be calculated by the following calculation formula. The specific formula of the digital signal abnormal index is as follows:

[0041]

[0042] Wherein, γhg represents the digital signal anomaly index, which is used to evaluate the degree of digital signal anomaly; αjk represents the digital signal time domain anomaly index, which is used to measure the degree of digital signal time domain anomaly; βbn represents the digital signal frequency domain anomaly index, which is used to measure the degree of digital signal frequency domain anomaly; δte represents the digital signal phase anomaly index, which is used to measure the degree of digital signal phase anomaly; ω1 represents the weight factor of the digital signal time domain anomaly index, which represents the influence of the digital signal time domain anomaly index; ω2 represents the weight factor of the digital signal frequency domain anomaly index, which represents the influence of the digital signal frequency domain anomaly index; ω3 represents the weight factor of the digital signal phase anomaly index, which represents the influence of the digital signal phase anomaly index.

[0043] In this implementation scheme, the weight factor of the digital signal time domain anomaly index is used to adjust the influence of the time domain anomaly index in the overall anomaly index. The larger the weight, the higher the degree of concern about the time domain anomaly. The weight factor of the digital signal frequency domain anomaly index is used to adjust the influence of the frequency domain anomaly index in the overall anomaly index. The larger the weight, the higher the degree of concern about the frequency domain anomaly. The weight factor of the digital signal phase anomaly index is used to adjust the influence of the phase anomaly index in the overall anomaly index. The larger the weight, the higher the degree of concern about the phase anomaly.

[0044] Specifically, the specific process of obtaining the digital signal time domain anomaly index is as follows: based on the digital signal time domain data, the digital signal amplitude, digital signal waveform time interval and digital signal period are differentiated and compared with the original carrier signal amplitude, original carrier signal waveform time interval and original carrier signal period, and ratio operations are performed with the allowable deviation value of the digital signal amplitude, the allowable deviation value of the digital signal waveform time interval and the allowable deviation value of the digital signal period, respectively; weights are assigned to the digital signal amplitude, digital signal waveform time interval and digital signal period, and comprehensive operations are performed with the differentiated ratios to obtain the digital signal time domain anomaly index.

[0045] In this implementation scheme, in addition to modeling the digital signal by the time series analysis method and obtaining the digital signal time domain anomaly index anomaly by analyzing the model residual, the digital signal time domain anomaly index can also be calculated by the following calculation formula. The specific formula of the digital signal time domain anomaly index is as follows:

[0046]

[0047] αjk represents the digital signal time domain anomaly index, which is used to measure the degree of digital signal time domain anomaly. TDa represents the digital signal amplitude, kut represents the digital signal waveform time interval, bkl represents the digital signal period, fq represents the original carrier signal amplitude, gh represents the original carrier signal waveform time interval, mb represents the original carrier signal period, Δtd represents the allowable deviation value of the digital signal amplitude, Δrt represents the allowable deviation value of the digital signal waveform time interval, Δew represents the allowable deviation value of the digital signal period, represents the weight coefficient of the digital signal amplitude, represents the weight coefficient of the digital signal amplitude, Represents the weight coefficient of the digital signal cycle. The allowable deviation value of the digital signal amplitude represents the maximum deviation value allowed for the digital signal amplitude, that is, the maximum deviation range of the amplitude. The allowable deviation value of the digital signal waveform time interval represents the maximum deviation value allowed for the digital signal waveform time interval, that is, the maximum deviation range of the waveform time interval. The allowable deviation value of the digital signal cycle represents the maximum deviation value allowed for the digital signal cycle, that is, the maximum deviation range of the cycle. The weight coefficient of the digital signal amplitude represents the weight coefficient of the digital signal amplitude, which is used to give importance to the amplitude when calculating the time domain anomaly index. The weight coefficient of the digital signal cycle represents the weight coefficient of the digital signal cycle, which is used to give importance to the cycle when calculating the time domain anomaly index. By monitoring the time domain characteristics of the signal and calculating the anomaly index, predictive maintenance of the equipment or system can be achieved. By analyzing the changing trend of the anomaly index, the health status of the equipment can be predicted, and maintenance measures can be taken in time to reduce downtime and maintenance costs. The time domain anomaly index can help identify abnormal events or abnormal behaviors in the signal. These anomalies may be caused by equipment failure, operational errors, malicious attacks, etc. By timely detecting and identifying anomalies, appropriate measures can be taken to repair or process them to avoid possible losses or risks.

[0048] Specifically, the specific process of obtaining the digital signal frequency domain anomaly index is as follows: based on the digital signal frequency domain data, weights are assigned to the power spectral density of the digital signal and the spectral density of the digital signal, and the power spectral density of the digital signal and the spectral density of the digital signal are subtracted from the power spectral density of the original carrier signal and the spectral density of the original carrier signal, and compared with the allowable deviation value of the power spectral density and the allowable deviation value of the spectral density, respectively, to obtain the digital signal frequency domain anomaly index.

[0049] In this implementation, in addition to obtaining the nonlinear characteristics and frequency domain interaction relationship in the signal through high-order spectrum analysis, the frequency domain anomaly index can be obtained by analyzing the high-order spectrum. The digital signal frequency domain anomaly index can also be calculated by the following calculation formula. The specific formula of the digital signal frequency domain anomaly index is as follows:

[0050]

[0051] βbn represents the frequency domain anomaly index of the digital signal, which is used to measure the degree of frequency domain anomaly of the digital signal. HYK represents the power spectrum density of the digital signal, VKL represents the spectrum density of the digital signal, GJ represents the power spectrum density of the original carrier signal, KL represents the spectrum density of the original carrier signal, ΔQF represents the allowable deviation value of the power spectrum density, ΔTH represents the allowable deviation value of the spectrum density, μ1 represents the weight factor of the power spectrum density of the digital signal, and μ2 represents the weight factor of the spectrum density of the digital signal; the allowable deviation value of the power spectrum density: represents the maximum allowable deviation value between the power spectrum density of the digital signal and the power spectrum density of the original carrier signal. The allowable deviation value of the spectrum density represents the maximum allowable deviation value between the spectrum density of the digital signal and the spectrum density of the original carrier signal. The weight factor of the power spectrum density of the digital signal represents the weight of the power spectrum density of the digital signal when calculating the frequency domain anomaly index, that is, its contribution to the anomaly index. The frequency domain anomaly index can be used to analyze the frequency characteristics of the signal. By analyzing the changing trend of the anomaly index, we can understand the frequency distribution of the signal and optimize the design and performance of the system. The frequency domain anomaly index can help identify abnormal frequency components or abnormal frequency distribution in the signal. The frequency domain anomaly index can be used to identify the noise components in the signal and take corresponding filtering or noise reduction measures. By removing abnormal frequency components, the clarity and quality of the signal can be improved.

[0052] Specifically, the specific process of obtaining the digital signal phase anomaly index is as follows: based on the digital signal phase data, the time delay of the digital signal, the phase difference of the digital signal and the phase offset of the digital signal are differentiated and compared with the time delay of the original carrier signal, the phase difference of the original carrier signal and the phase offset of the original carrier signal, and ratio operations are performed with the allowable deviation value of the time delay of the digital signal, the allowable deviation value of the phase difference of the digital signal and the allowable deviation value of the phase offset of the digital signal, and a comprehensive operation is performed to obtain the digital signal phase anomaly index.

[0053] In this implementation, in addition to extracting the phase information of the signal and evaluating the digital signal phase anomaly index by using the adaptive filtering technology, the digital signal phase anomaly index can also be calculated by the following calculation formula. The specific formula of the digital signal phase anomaly index is as follows:

[0054]

[0055] δte represents the digital signal phase anomaly index, which is used for the digital signal phase anomaly degree, DTD represents the time delay of the digital signal, YPK represents the phase difference of the digital signal, POH represents the phase offset of the digital signal, SCV represents the time delay of the original carrier signal, NC represents the phase difference of the original carrier signal, FH represents the phase offset of the original carrier signal, ΔBJ represents the allowable deviation value of the time delay of the digital signal, ΔKN represents the allowable deviation value of the phase difference of the digital signal, and ΔFJ represents the allowable deviation value of the phase offset of the digital signal. The time delay of the original carrier signal represents the time delay amount of the original carrier signal relative to a certain reference point, which is used to compare with the time delay of the digital signal. The phase difference of the original carrier signal represents the difference between the original carrier signal and a certain reference signal in phase, which is used to compare with the phase difference of the digital signal. The phase offset of the original carrier signal represents the phase offset amount of the original carrier signal relative to a certain reference signal, which is used to compare with the phase offset of the digital signal. The allowable deviation value of the time delay of the digital signal represents the maximum deviation value allowed between the time delay of the digital signal and the time delay of the original carrier signal. The allowed deviation value of the phase difference of the digital signal indicates the maximum allowed deviation value between the phase difference of the digital signal and the phase difference of the original carrier signal. The allowed deviation value of the phase offset of the digital signal indicates the maximum allowed deviation value between the phase offset of the digital signal and the phase offset of the original carrier signal. The phase anomaly index can help detect the phase anomaly of the signal in the frequency domain, that is, the phase characteristics of the signal deviate or are abnormal from the expected phase characteristics under normal circumstances. This deviation is caused by signal damage, equipment failure, and noise interference. By calculating the phase anomaly index, the phase characteristics of the signal can be analyzed and evaluated. This helps to understand the phase distribution of the signal, the degree of phase offset, and other information, and provides a reference for system design and performance optimization. The trend of the phase anomaly index can indicate whether phase correction or adjustment is required. For signals with large phase anomalies, corresponding correction measures may be required to ensure the stability and accuracy of the signal.

[0056] Specifically, based on the digital signal anomaly index, the abnormal digital signal is removed, and the specific process of automatically correcting the valid digital signal is as follows: compare the digital signal anomaly index with the standard value of the digital signal anomaly index, identify the abnormal data points in the digital signal, and delete the abnormal data points from the digital signal data set; automatically correct the valid digital signal through a correction algorithm, and the automatic correction operation includes calibration, adjustment and correction to keep it consistent with the standard signal.

[0057] In this embodiment, the abnormal index of the digital signal is compared with a pre-set standard value. The abnormal index can be determined by some algorithms or rules, and it reflects the abnormal degree of the signal. By comparing the abnormal index with the standard value, it is possible to identify which data points in the digital signal are abnormal. Once the abnormal data points are identified, they will be deleted from the digital signal data set. This step is to purify the data and remove the abnormal data that interferes with subsequent processing and analysis. After the abnormal data points are deleted, the remaining valid digital signal will be sent to the automatic correction algorithm for processing. The automatic correction operation usually includes calibration, adjustment and correction, and the purpose is to adjust the valid signal to a state consistent with the standard signal. This can be achieved through a series of mathematical algorithms, filters, calibrators, etc.

[0058] Example 2

[0059] See also Figure 2 A low-cost HDX characteristic frequency rapid measurement device includes the following modules: a signal acquisition module, a filtering and conversion module, a data processing module, a frequency calculation module, and a frequency output module; the signal acquisition module is used to extract and retain the measurement signal sent by the HDX tag through the signal acquisition device; the filtering and conversion module is used to remove the original carrier signal through the filtering circuit, use the digital shaping circuit to convert the filtered signal into a digital signal, and perform preprocessing to obtain a digital signal data set; the data processing module is used to perform data analysis on the digital signal data set based on the convolutional neural network algorithm, obtain the digital signal anomaly index, remove the abnormal digital signal based on the digital signal anomaly index, and automatically correct the effective digital signal; the frequency calculation module is used to perform Fourier transform and spectrum analysis on the digital signal after automatic correction to extract frequency characteristics, and obtain the characteristic frequency sent by the HDX tag; the frequency output module is used to output the characteristic frequency sent by the HDX tag.

[0060] In summary, this application has at least the following effects:

[0061] The low-cost HDX characteristic frequency rapid measurement method and device compare the digital signal with the original carrier signal, adopt the convolutional neural network algorithm to analyze the data, remove the abnormal sampling data, and automatically correct the effective sampling data, so as to restore the interfered digital signal to the state before the interference, and then use the corrected data to perform spectrum analysis, so as to improve the measurement accuracy and efficiency, reduce the equipment cost, and be suitable for a variety of application scenarios. By setting the signal acquisition module, the filtering and conversion module, the data processing module, the frequency calculation module, and the frequency output module, and only using the low-cost MPU as the operation unit, the cost of the whole device is greatly reduced, the equipment cost is reduced, and the measurement efficiency and reliability are improved.

[0062] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0063] Example 3

[0064] An electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the low-cost HDX characteristic frequency fast measurement method in embodiment 1 is implemented.

[0065] Embodiment 3 of the present invention is merely an example and should not limit the functions and scope of use of the embodiments of the present invention.

[0066] The electronic device may be in the form of a general-purpose computing device, for example, it may be a server device. The components of the electronic device may include, but are not limited to: at least one processor, at least one memory, and a bus connecting different system components (including the memory and the processor).

[0067] The bus includes data bus, address bus and control bus.

[0068] The memory may include volatile memory, such as random access memory (RAM) and / or cache memory, and may further include read-only memory (ROM).

[0069] The memory may also include a program tool having a set (at least one) of program modules, such program modules including but not limited to: an operating system, one or more application programs, other program modules and program data, each of which or some combination may include the implementation of a network environment.

[0070] The processor executes various functional applications and data processing by running computer programs stored in the memory.

[0071] The electronic device can also communicate with one or more external devices (e.g., keyboard, pointing device, etc.). Such communication can be performed through an input / output (I / O) interface. In addition, the electronic device can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN) and / or public network, such as the Internet) through a network adapter. The network adapter communicates with other modules of the electronic device through a bus. It should be understood that, although not shown in the figure, other hardware and / or software modules can be used in conjunction with the electronic device, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID (disk array) systems, tape drives, and data backup storage systems, etc.

[0072] It should be noted that although several units / modules or sub-units / modules of the electronic device are mentioned in the above detailed description, this division is merely exemplary and not mandatory. In fact, according to the embodiments of the present application, the features and functions of two or more units / modules described above can be embodied in one unit / module. Conversely, the features and functions of one unit / module described above can be further divided into multiple units / modules to be embodied.

[0073] Example 4

[0074] A computer-readable storage medium stores a computer program, and when the program is executed by a processor, the steps of the low-cost HDX characteristic frequency fast measurement method in embodiment 1 are implemented.

[0075] The readable storage medium may include but is not limited to: a portable disk, a hard disk, a random access memory, a read-only memory, an erasable programmable read-only memory, an optical storage device, a magnetic storage device or any suitable combination of the above.

[0076] In a possible implementation, the present invention can also be implemented in the form of a program product, which includes program code. When the program product runs on a terminal device, the program code is used to enable the terminal device to execute the steps of implementing the low-cost HDX characteristic frequency fast measurement method described in Example 1.

[0077] The program code for executing the present invention may be written in any combination of one or more programming languages, and may be executed entirely on a user device, partially on a user device, as an independent software package, partially on a user device and partially on a remote device, or entirely on a remote device.

Claims

1. A low-cost HDX characteristic frequency rapid measurement method, characterized in that: The following steps are involved: S1. Extract and retain the measurement signal sent by the HDX tag through the signal acquisition device; S2. removing the original carrier signal through a filtering circuit, converting the filtered measurement signal into a digital signal using a digital shaping circuit, and performing preprocessing to obtain a digital signal data set; S3. Based on the convolutional neural network algorithm, the digital signal data set is analyzed to obtain the digital signal anomaly index. Based on the digital signal anomaly index, the abnormal digital signal is removed and the effective digital signal is automatically corrected; S4. Perform Fourier transform and spectrum analysis on the digital signal after automatic correction to extract frequency characteristics and obtain the characteristic frequency sent by the HDX tag; S5. Output the characteristic frequency sent by the HDX tag; Based on the convolutional neural network algorithm, the specific process of data analysis on the digital signal data set is as follows: Based on the convolutional neural network algorithm, a digital signal comparison model is established through the comparison relationship between the original carrier signal and the digital signal to obtain the characteristic data of the digital signal. The characteristic data of the digital signal includes digital signal time domain data, digital signal frequency domain data, and digital signal phase data; The digital signal time domain data includes digital signal amplitude, digital signal waveform time interval and digital signal period; The digital signal frequency domain data includes the power spectrum density of the digital signal and the spectrum density of the digital signal; The digital signal phase data includes a time delay of the digital signal, a phase difference of the digital signal and a phase offset of the digital signal; The specific process of establishing the digital signal comparison model is as follows: The original carrier signal and the corresponding digital signal data set are obtained as training data sets, and time domain feature extraction and frequency domain feature extraction are performed. The original carrier signal and the corresponding digital signal data set are marked, and the original carrier signal and the digital signal are used as a pair of input and output. The convolutional neural network supervised learning algorithm is used as a comparison model to learn the mapping relationship between the original carrier signal and the digital signal, and establish a digital signal comparison model; The specific process of obtaining the digital signal anomaly index is as follows: Based on the digital signal time domain data, the original carrier signal is compared with the digital signal in the time domain to obtain the digital signal time domain anomaly index; Based on the frequency domain data of the digital signal, the original carrier signal is compared with the digital signal in the frequency domain to obtain the frequency domain anomaly index of the digital signal; Based on the digital signal phase data, the phase of the original carrier signal is compared with the phase of the digital signal to obtain the digital signal phase anomaly index; Assign weights to the acquired digital signal time domain anomaly index, digital signal frequency domain anomaly index and digital signal phase anomaly index, and perform comprehensive calculations to obtain the digital signal anomaly index; The specific formula of the digital signal anomaly index is as follows: ； In the formula, Indicates the digital signal anomaly index, Represents the digital signal time domain anomaly index, Represents the frequency domain anomaly index of the digital signal, Indicates the digital signal phase anomaly index, Represents the weight factor of the digital signal time domain anomaly index, Represents the weight factor of the frequency domain anomaly index of the digital signal, Represents the weight factor of the digital signal phase anomaly index.

2. A low-cost HDX characteristic frequency rapid measurement method according to claim 1, characterized in that: The specific process of obtaining the digital signal time domain anomaly index is as follows: Based on the digital signal time domain data, the digital signal amplitude, the digital signal waveform time interval and the digital signal period are differentially compared with the original carrier signal amplitude, the original carrier signal waveform time interval and the original carrier signal period, and the digital signal amplitude, the digital signal waveform time interval and the original carrier signal period are ratio-calculated with the allowable deviation value of the digital signal amplitude, the allowable deviation value of the digital signal waveform time interval and the allowable deviation value of the digital signal period; Weights are assigned to the digital signal amplitude, digital signal waveform time interval and digital signal period, and a comprehensive calculation is performed with the differentiation ratio to obtain the digital signal time domain anomaly index.

3. The low-cost HDX characteristic frequency rapid measurement method according to claim 1, characterized in that: The specific process of obtaining the digital signal frequency domain anomaly index is as follows: Based on the frequency domain data of the digital signal, weights are assigned to the power spectral density of the digital signal and the spectral density of the digital signal. The power spectral density of the digital signal and the spectral density of the digital signal are subtracted from the power spectral density of the original carrier signal and the spectral density of the original carrier signal, and they are compared with the allowable deviation values ​​of the power spectral density and the allowable deviation values ​​of the spectral density, respectively, to obtain the digital signal frequency domain anomaly index.

4. The low-cost HDX characteristic frequency rapid measurement method according to claim 1, characterized in that: The specific process of obtaining the digital signal phase anomaly index is as follows: Based on the digital signal phase data, the time delay, phase difference and phase offset of the digital signal are differentially compared with the time delay, phase difference and phase offset of the original carrier signal, and ratio operations are performed with the allowable deviation value of the time delay, phase difference and phase offset of the digital signal, and comprehensive operations are performed to obtain the digital signal phase anomaly index.

5. The low-cost HDX characteristic frequency rapid measurement method according to claim 1, characterized in that: The specific process of removing abnormal digital signals based on the digital signal abnormality index and automatically correcting the valid digital signals is as follows: Compare the digital signal anomaly index with a standard value of the digital signal anomaly index, identify abnormal data points in the digital signal, and delete the abnormal data points from the digital signal data set; The effective digital signal is automatically corrected through a correction algorithm. The automatic correction operation includes calibration, adjustment and correction to keep it consistent with the standard signal.

6. A low-cost HDX characteristic frequency rapid measurement device, characterized by: It includes the following modules: signal acquisition module, filtering and conversion module, data processing module, frequency calculation module, frequency output module; The signal acquisition module is used to extract and retain the measurement signal sent by the HDX tag through the signal acquisition device; The filtering and conversion module is used to remove the original carrier signal through the filtering circuit, convert the filtered signal into a digital signal using the digital shaping circuit, and perform preprocessing to obtain a digital signal data set; The data processing module is used to perform data analysis on the digital signal data set based on the convolutional neural network algorithm, obtain the digital signal anomaly index, remove the abnormal digital signal based on the digital signal anomaly index, and automatically correct the effective digital signal; The frequency calculation module is used to perform Fourier transform and spectrum analysis on the digital signal after automatic deviation correction to extract frequency characteristics and obtain the characteristic frequency sent by the HDX tag; The frequency output module is used to output the characteristic frequency sent by the HDX tag; Based on the convolutional neural network algorithm, the specific process of data analysis on the digital signal data set is as follows: Based on the convolutional neural network algorithm, a digital signal comparison model is established through the comparison relationship between the original carrier signal and the digital signal to obtain the characteristic data of the digital signal. The characteristic data of the digital signal includes digital signal time domain data, digital signal frequency domain data, and digital signal phase data; The digital signal time domain data includes digital signal amplitude, digital signal waveform time interval and digital signal period; The digital signal frequency domain data includes the power spectrum density of the digital signal and the spectrum density of the digital signal; The digital signal phase data includes a time delay of the digital signal, a phase difference of the digital signal and a phase offset of the digital signal; The specific process of establishing the digital signal comparison model is as follows: The original carrier signal and the corresponding digital signal data set are obtained as training data sets, and time domain feature extraction and frequency domain feature extraction are performed. The original carrier signal and the corresponding digital signal data set are marked, and the original carrier signal and the digital signal are used as a pair of input and output. The convolutional neural network supervised learning algorithm is used as a comparison model to learn the mapping relationship between the original carrier signal and the digital signal, and establish a digital signal comparison model; The specific process of obtaining the digital signal anomaly index is as follows: Based on the digital signal time domain data, the original carrier signal is compared with the digital signal in the time domain to obtain the digital signal time domain anomaly index; Based on the frequency domain data of the digital signal, the original carrier signal is compared with the digital signal in the frequency domain to obtain the frequency domain anomaly index of the digital signal; Based on the digital signal phase data, the phase of the original carrier signal is compared with the phase of the digital signal to obtain the digital signal phase anomaly index; Assign weights to the acquired digital signal time domain anomaly index, digital signal frequency domain anomaly index and digital signal phase anomaly index, and perform comprehensive calculations to obtain the digital signal anomaly index; The specific formula of the digital signal anomaly index is as follows: ； In the formula, Indicates the digital signal anomaly index, Represents the digital signal time domain anomaly index, Represents the frequency domain anomaly index of the digital signal, Indicates the digital signal phase anomaly index, Represents the weight factor of the digital signal time domain anomaly index, Represents the weight factor of the frequency domain anomaly index of the digital signal, Represents the weight factor of the digital signal phase anomaly index.

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