Object evaluation method and device, computer equipment, readable storage medium and program product

Abstract

The invention relates to an object evaluation method and device, computer equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: acquiring a to-be-evaluated channel response matrix and a standard spectrum matrix; converting the to-be-evaluated channel response matrix into a first image, and performing frequency domain conversion on the first image to obtain a target frequency spectrum matrix; and determining a channel quality evaluation result based on the target spectrum matrix and the standard spectrum matrix. By adopting the method, the complex waveform information contained in the to-be-evaluated channel response matrix can be extracted and converted into the frequency domain information, the calculation processing of complex signal evaluation can be simplified, and complex point-by-point calculation and statistical analysis are avoided, so that the calculation time and complexity are remarkably reduced, the efficiency of data quality evaluation is greatly improved, and the method is suitable for popularization and application. The method is suitable for real-time and large-scale data processing.

Description

Technical Field

[0001] The present application relates to the field of communication technology, and in particular to an object evaluation method, apparatus, computer equipment, computer-readable storage medium, and computer program product. Background Art

[0002] With the development of communication technology, channel quality assessment has become particularly important in the design, optimization and maintenance of communication systems.

[0003] Traditionally, the Channel Response Matrix (CRM) is statistically analyzed. After obtaining the CRM, the matrix is ​​parsed to determine the various influences experienced by the signal during transmission. Statistical calculations are then performed to obtain the performance parameters of the CRM, such as channel gain, average signal-to-noise ratio, delay, and multipath components. These parameters are then used as evaluation parameters for the CRM to determine the channel quality assessment result.

[0004] However, in current traditional technologies, with the development of communication systems, the complexity of communication systems increases, and the data dimension of the channel response matrix also increases, resulting in an increase in the amount of calculation of performance parameters in the channel response matrix, and thus, resulting in low efficiency in evaluating channel quality. Summary of the Invention

[0005] Based on this, it is necessary to provide an object evaluation method, apparatus, computer device, computer-readable storage medium and computer program product to address the above technical issues.

[0006] In a first aspect, the present application provides an object evaluation method, comprising:

[0007] Obtaining a channel response matrix to be evaluated and a standard spectrum matrix;

[0008] Converting the channel response matrix to be evaluated into a first image, and performing frequency domain conversion on the first image to obtain a target spectrum matrix;

[0009] A channel quality evaluation result is determined based on the target spectrum matrix and the standard spectrum matrix.

[0010] In one embodiment, before obtaining the channel response matrix to be evaluated and the standard spectrum matrix, the method further includes:

[0011] Obtain a standard channel response matrix;

[0012] The standard channel response matrix is ​​converted into a second image, and the second image is Fourier transformed to obtain a standard spectrum matrix.

[0013] In one embodiment, converting the channel response matrix to be evaluated into a first image includes:

[0014] Determine a real matrix and an imaginary matrix corresponding to the channel response matrix to be evaluated;

[0015] A first image corresponding to the channel response matrix to be evaluated is generated according to the real matrix and the imaginary matrix.

[0016] In one embodiment, performing frequency domain conversion on the first image to obtain a target spectrum matrix includes:

[0017] Performing Fourier transform on the real matrix and the imaginary matrix of the first image respectively to obtain a first spectrum matrix and a second spectrum matrix;

[0018] A target spectrum matrix is ​​constructed according to the first spectrum matrix and the second spectrum matrix.

[0019] In one embodiment, performing Fourier transform on the real matrix and the imaginary matrix of the first image to obtain the first spectrum matrix and the second spectrum matrix respectively includes:

[0020] Initializing the real matrix and the imaginary matrix of the first image respectively to obtain a first initial matrix and a second initial matrix;

[0021] For each spatial index, calculating a first phase factor corresponding to the real matrix and a second phase factor corresponding to the imaginary matrix;

[0022] Obtaining a first weighted pixel value according to the first phase factor of each of the spatial indexes, and obtaining a second weighted pixel value according to the second phase factor of each of the spatial indexes;

[0023] The first weighted pixel values ​​are accumulated according to the first initial matrix to obtain a first spectrum matrix, and the second weighted pixel values ​​are accumulated according to the second initial matrix to obtain a second spectrum matrix.

[0024] In one embodiment, determining a channel quality assessment result based on the target spectrum matrix and the standard spectrum matrix includes:

[0025] Calculate a first probability density distribution of the target spectrum matrix and a second probability density distribution of the standard spectrum matrix based on the target spectrum matrix and the standard spectrum matrix respectively;

[0026] Calculating a first spectrum entropy corresponding to the first probability density distribution, and calculating a second spectrum entropy corresponding to the second probability density distribution;

[0027] A channel quality assessment result is determined according to the first spectrum entropy and the second spectrum entropy.

[0028] In a second aspect, the present application further provides an object evaluation device, comprising:

[0029] A first acquisition module is used to acquire a channel response matrix to be evaluated and a standard spectrum matrix;

[0030] A first frequency domain conversion module is configured to convert the channel response matrix to be evaluated into a first image, and perform frequency domain conversion on the first image to obtain a target spectrum matrix;

[0031] An evaluation module is configured to determine a channel quality evaluation result based on the target spectrum matrix and the standard spectrum matrix.

[0032] In one embodiment, the apparatus further comprises:

[0033] A second acquisition module is used to obtain a standard channel response matrix;

[0034] The second frequency domain conversion module is configured to convert the standard channel response matrix into a second image, and perform Fourier transform on the second image to obtain a standard frequency spectrum matrix.

[0035] In one embodiment, the first frequency domain conversion module is specifically configured to determine a real matrix and an imaginary matrix corresponding to the channel response matrix to be evaluated;

[0036] A first image corresponding to the channel response matrix to be evaluated is generated according to the real matrix and the imaginary matrix.

[0037] In one embodiment, the first frequency domain conversion module is specifically configured to perform Fourier transform on the real matrix and the imaginary matrix of the first image respectively to obtain a first spectrum matrix and a second spectrum matrix;

[0038] A target spectrum matrix is ​​constructed according to the first spectrum matrix and the second spectrum matrix.

[0039] In one embodiment, the first frequency domain conversion module is specifically configured to initialize the real matrix and the imaginary matrix of the first image respectively to obtain a first initial matrix and a second initial matrix;

[0040] For each spatial index, calculating a first phase factor corresponding to the real matrix and a second phase factor corresponding to the imaginary matrix;

[0041] Obtaining a first weighted pixel value according to the first phase factor of each of the spatial indexes, and obtaining a second weighted pixel value according to the second phase factor of each of the spatial indexes;

[0042] The first weighted pixel values ​​are accumulated according to the first initial matrix to obtain a first spectrum matrix, and the second weighted pixel values ​​are accumulated according to the second initial matrix to obtain a second spectrum matrix.

[0043] In one embodiment, the evaluation module is specifically configured to calculate a first probability density distribution of the target spectrum matrix and a second probability density distribution of the standard spectrum matrix based on the target spectrum matrix and the standard spectrum matrix respectively;

[0044] Calculating a first spectrum entropy corresponding to the first probability density distribution, and calculating a second spectrum entropy corresponding to the second probability density distribution;

[0045] A channel quality assessment result is determined according to the first spectrum entropy and the second spectrum entropy.

[0046] In a third aspect, the present application further provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0047] Obtaining a channel response matrix to be evaluated and a standard spectrum matrix;

[0048] Converting the channel response matrix to be evaluated into a first image, and performing frequency domain conversion on the first image to obtain a target spectrum matrix;

[0049] A channel quality evaluation result is determined based on the target spectrum matrix and the standard spectrum matrix.

[0050] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the following steps are implemented:

[0051] Obtaining a channel response matrix to be evaluated and a standard spectrum matrix;

[0052] Converting the channel response matrix to be evaluated into a first image, and performing frequency domain conversion on the first image to obtain a target spectrum matrix;

[0053] A channel quality evaluation result is determined based on the target spectrum matrix and the standard spectrum matrix.

[0054] In a fifth aspect, the present application further provides a computer program product, comprising a computer program, which, when executed by a processor, implements the following steps:

[0055] Obtaining a channel response matrix to be evaluated and a standard spectrum matrix;

[0056] Converting the channel response matrix to be evaluated into a first image, and performing frequency domain conversion on the first image to obtain a target spectrum matrix;

[0057] A channel quality evaluation result is determined based on the target spectrum matrix and the standard spectrum matrix.

[0058] The above-mentioned object evaluation method, apparatus, computer equipment, computer-readable storage medium, and computer program product obtain a channel response matrix to be evaluated and a standard spectrum matrix; convert the channel response matrix to be evaluated into a first image, and perform frequency domain conversion on the first image to obtain a target spectrum matrix; and determine the channel quality evaluation result based on the target spectrum matrix and the standard spectrum matrix. This method converts the high-dimensional channel response matrix to be evaluated into an image form and processes it using the frequency domain conversion method of image processing and signal processing. Compared with traditional analytical methods, the frequency domain conversion method can quickly extract the key features and frequency domain information of the channel response matrix, extract and convert the complex waveform information contained in the channel response matrix to be evaluated into frequency domain information, simplify the computational processing of complex signal evaluation, avoid complex point-by-point calculations and statistical analysis, thereby significantly reducing the computational time and complexity, greatly improving the efficiency of data quality evaluation, and being suitable for real-time and large-scale data processing. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following briefly introduces the drawings required for use in the embodiments of the present application or related technical descriptions. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying any creative work.

[0060] Figure 1 1 is a flow chart of an object evaluation method according to an embodiment;

[0061] Figure 2 A schematic diagram of a process for constructing a standard spectrum matrix in one embodiment;

[0062] Figure 3 FIG4 is a flow chart of converting a channel response matrix to be evaluated into a first image in one embodiment;

[0063] Figure 4 FIG. 1 is a schematic diagram of a process for constructing a target spectrum matrix according to a first image in one embodiment;

[0064] Figure 5 FIG4 is a flow chart of calculating a target spectrum matrix according to a real matrix and an imaginary matrix of a first image in one embodiment;

[0065] Figure 6FIG1 is a flow chart of evaluating a channel response matrix to be evaluated based on a target spectrum matrix and a standard spectrum matrix in one embodiment;

[0066] Figure 7 A flowchart illustrating an example of an object evaluation method according to an embodiment;

[0067] Figure 8 is a structural block diagram of an object evaluation device in one embodiment;

[0068] Figure 9 FIG. 1 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0069] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0070] The channel response matrix is ​​a key parameter that describes the channel characteristics in a multiple-input multiple-output (MIMO) system. It reflects the attenuation, interference, and multipath effects experienced by the signal during transmission.

[0071] Traditional channel quality assessment methods are typically based on analyzing channel statistics or calculating channel capacity. In communication systems, the statistical characteristics of a communication channel describe the random interference and fading properties of signals during transmission. Statistical characteristics can be analyzed by analyzing the channel response matrix to obtain parameters such as channel gain, delay, and multipath components, thereby assessing the quality and reliability of the communication channel. Channel capacity refers to the maximum information transmission rate achievable under given channel conditions and can be calculated using Shannon's theorem.

[0072] However, these traditional methods fail to fully capture the complex spatial and frequency characteristics inherent in the channel response matrix. Furthermore, as the complexity of communication systems increases, the data dimensionality of the channel response matrix also increases, limiting the computational efficiency and accuracy of these traditional methods. The channel response matrix is ​​a complex matrix containing a wealth of information, characterized by both spatial dimensions (number of antennas) and frequency dimensions (spectral distribution). Translating the complex data of the channel response matrix into quantifiable quality metrics is a key issue. Traditional methods focus primarily on channel modeling and prediction, but lack quality assessment criteria based on actual channel response data. A method is needed to extract statistical information from the data characteristics of the channel response matrix and provide quantitative metrics. Furthermore, the wireless communication environment is rapidly changing, requiring data quality assessment methods to be not only accurate but also real-time and efficient to meet the demands of real-world scenarios.

[0073] This application converts the high-dimensional channel response matrix to be evaluated into an image form and processes it using the frequency domain conversion method of image processing and signal processing. Compared with the traditional analytical method, the frequency domain conversion method can quickly extract the key features and frequency domain information of the channel response matrix, extract the complex waveform information contained in the channel response matrix to be evaluated and convert it into frequency domain information, which can simplify the computational processing of complex signal evaluation and avoid complex point-by-point calculations and statistical analysis, thereby significantly reducing the computing time and complexity, greatly improving the efficiency of data quality evaluation, and being suitable for real-time and large-scale data processing. At the same time, through spectral analysis, the frequency domain characteristics of the channel data are deeply explored and accurately compared with the standard spectrum matrix of the theoretical model, which can accurately evaluate the data quality and identify potential problems in the communication channel.

[0074] In one embodiment, Figure 1 As shown, an object evaluation method is provided. This embodiment uses the method applied to a terminal as an example. The terminal can be an independent terminal for evaluating a communication channel, a base station terminal, a central controller terminal, or a network management unit terminal in a wireless communication system. It is understood that the method can also be applied to a server, or to a system including a terminal and a server, and implemented through interaction between the terminal and the server. In this embodiment, the method includes the following steps:

[0075] Step 102: Obtain a channel response matrix to be evaluated and a standard spectrum matrix.

[0076] In the embodiments of this application, the channel response matrix to be evaluated is a key parameter that describes the channel characteristics between the transmitter and receiver. To evaluate data quality, the terminal first needs to obtain and represent the channel response matrix and the standard spectrum matrix. This matrix incorporates various influences experienced by the signal during transmission, such as attenuation, phase shift, and multipath.

[0077] In a multiple-input multiple-output (MIMO) system, the transmitter and receiver each include multiple base station antennas. The CRM is a matrix, and each element in the channel response matrix represents the channel response from one transmitting antenna to one receiving antenna. The channel response matrix to be evaluated can receive and process the test signal transmitted by the transmitting antenna through the base station or mobile terminal. The receiving base station or mobile terminal estimates the channel response from each transmitting antenna to each receiving antenna based on the known training sequence and the received signal through a certain algorithm (for example, least squares method, minimum mean square error, etc.), and then combines the channel response from each transmitting antenna to each receiving antenna into a matrix, namely the channel response matrix to be evaluated. Among them, the number of rows of the channel response matrix to be evaluated corresponds to the number of receiving antennas, and the number of columns corresponds to the number of transmitting antennas. The channel response matrix to be evaluated H is defined as:

[0078]

[0079] Among them, the elements Indicates that from The transmitting antenna to the The channel gain of the receiving antennas, Usually complex numbers, containing amplitude and phase information, It can be expressed as:

[0080]

[0081] in, is the real part of the channel gain, ) is the imaginary part of the channel gain.

[0082] The standard spectrum matrix is ​​a reference spectrum matrix under ideal conditions in the application scenario of the channel response matrix to be evaluated. It is used to describe the channel frequency response characteristics under ideal channel conditions (no noise, no interference, and no distortion) for comparison with the actual channel response.

[0083] In a specific embodiment, if the channel response matrix to be evaluated is:

[0084]

[0085] in, , indicating that the signal from Tx1 (transmitting antenna 1) to Rx1 (receiving antenna 1) experiences an attenuation of 0.8 and a phase shift of 0.2; , indicating that the signal from Tx2 (transmitting antenna 2) to Rx1 (receiving antenna 1) experiences an attenuation of 0.6 and a phase shift of -0.1.

[0086] Step 104 : converting the channel response matrix to be evaluated into a first image, and performing frequency domain conversion on the first image to obtain a target spectrum matrix.

[0087] In an embodiment of the present application, the channel response matrix contains a large amount of data, and directly analyzing the channel response matrix is ​​relatively complex. Therefore, the terminal converts the channel response matrix into a first image, which can make the data features contained in the channel response matrix more intuitive. Furthermore, the terminal maps each element in the channel response matrix to be evaluated to a pixel value of the image to generate the first image. For example, the amplitude value and phase value in the channel response matrix to be evaluated can be converted to the grayscale level of the pixel, thereby generating a grayscale image. For a MIMO system, the channel response of each antenna pair can be plotted as a pixel point of the first image.

[0088] After obtaining the first image based on the channel response matrix to be evaluated, the terminal performs a frequency domain conversion on the first image using a frequency domain analysis method to obtain a target spectrum matrix corresponding to the channel response matrix to be evaluated. For example, the terminal performs a one-dimensional frequency domain conversion on each row or column of the first image to obtain a frequency domain representation, and then rearranges the frequency domain representations representing the rows and columns to generate the target spectrum matrix.

[0089] Frequency domain analysis methods are typically used to convert an image (i.e., a time-domain or spatial-domain representation of the channel response) into a frequency-domain representation, obtaining the amplitude and phase information for each frequency component in the image. Therefore, converting the channel response matrix to be evaluated into a first image allows the terminal to more intuitively analyze channel performance and simplify the data analysis process, resulting in a target spectrum matrix. The target spectrum matrix describes the intensity and phase of each frequency component in the original first image. The target spectrum matrix encompasses the channel response characteristics at each frequency point and serves as the basis for subsequent channel quality assessment.

[0090] Step 106: Determine a channel quality assessment result based on the target spectrum matrix and the standard spectrum matrix.

[0091] In an embodiment of the present application, the terminal calculates the difference between the target spectrum matrix and the standard spectrum matrix, for example, calculating the amplitude error and phase error, and thereby quantifies the difference between the actual channel and the ideal channel to assess the channel quality. For example, spectral entropy is calculated based on the target spectrum matrix and the standard spectrum matrix, and the difference between the spectral entropies is used as the quality assessment result.

[0092] In the above-mentioned object evaluation method, the high-dimensional channel response matrix to be evaluated is converted into an image form and processed using the frequency domain conversion method of image processing and signal processing. Compared with the traditional analytical method, the frequency domain conversion method can quickly extract the key features and frequency domain information of the channel response matrix, extract the complex waveform information contained in the channel response matrix to be evaluated and convert it into frequency domain information, which can simplify the computational processing of complex signal evaluation and avoid complex point-by-point calculations and statistical analysis, thereby significantly reducing the computing time and complexity, greatly improving the efficiency of data quality assessment, and being suitable for real-time and large-scale data processing. At the same time, through spectral analysis, the frequency domain characteristics of the channel data are deeply explored and accurately compared with the standard spectral matrix of the theoretical model, which can accurately evaluate the data quality and identify potential problems in the communication channel.

[0093] In an exemplary embodiment, Figure 2 As shown, before step 102, the method further includes steps 202 to 204. Among them:

[0094] Step 202: Acquire a standard channel response matrix.

[0095] In embodiments of the present application, a standard spectrum matrix may be a channel response matrix under ideal conditions, pre-constructed for different scenario configuration information. The scenario configuration information includes the channel model, antenna configuration (such as the number and arrangement of antennas in a MIMO system), frequency range, bandwidth, and the like. An ideal channel model can be pre-constructed based on the scenario configuration information. A terminal may obtain a standard channel response matrix based on the channel response matrix under ideal conditions defined in the protocol standard. For example, in an indoor office environment, line-of-sight propagation paths typically exist, and the channel response primarily consists of direct paths and a small number of reflected paths. Therefore, a standard channel response matrix for the indoor office environment is constructed based on the scenario configuration information for the indoor office environment. In an urban microcellular environment, line-of-sight propagation paths typically do not exist, and the channel response primarily consists of multipath components. Therefore, a standard channel response matrix for the microcellular environment is constructed based on the scenario configuration information for the indoor microcellular environment. In a high-speed railway environment, high speeds result in significant Doppler shift, causing the channel response to change rapidly over time. Therefore, a standard channel response matrix for the high-speed railway environment is constructed based on the scenario configuration information for the high-speed railway environment.

[0096] Step 204 : Convert the standard channel response matrix into a second image, and perform Fourier transform on the second image to obtain a standard spectrum matrix.

[0097] In an embodiment of the present application, since the standard channel response matrix is ​​a complex data structure and direct analysis is difficult, the terminal can convert it into an image form to obtain a second image, which can more intuitively display the characteristics of the channel under ideal conditions. Specifically, the terminal uses the real part and the imaginary part of the standard channel response matrix as two channels of the image, and maps them to obtain a second image. Optionally, if the image is a grayscale image, the amplitude or weighted value of the real part and the imaginary part can be mapped to brightness. If the image is a color image, the real part and the imaginary part can be mapped to different color channels, for example, the real part of the standard channel response matrix is ​​mapped to the blue channel, and the imaginary part is mapped to the red channel.

[0098] The Fourier transform is a mathematical tool that converts signals from the time domain to the frequency domain. It can be used to analyze the characteristics of the signal in the standard channel response matrix at different frequencies. The terminal performs a Fourier transform on the second image in different scenarios to obtain a standard spectrum matrix, which is stored and used as a reference for quality assessment of the channel response matrix to be identified. The detailed process of performing the Fourier transform is described in the following embodiments.

[0099] In this embodiment, a standard channel response matrix is ​​pre-constructed based on different scenario configuration information, converted into a second image, and then Fourier transformed to obtain a standard spectrum matrix. This helps extract the spectral characteristics of the ideal channel. The standard spectrum matrix is ​​used as a reference standard and compared with the spectrum matrix of the channel response matrix to be evaluated. Channel quality can be quantitatively assessed by calculating indicators such as spectral entropy. The standard spectrum matrix represents the frequency distribution pattern of the ideal channel. By comparing it with the spectrum of the actual channel, factors affecting the channel, such as noise, interference, and attenuation, can be accurately identified, thereby improving the accuracy of channel quality assessment.

[0100] In an exemplary embodiment, Figure 3 As shown, step 104 includes steps 302 to 304. Among them:

[0101] Step 302: Determine the real part matrix and the imaginary part matrix corresponding to the channel response matrix to be evaluated.

[0102] In this embodiment of the present application, to evaluate the channel response matrix to be evaluated using image processing and frequency domain analysis techniques, the terminal first treats the real and imaginary parts of the channel response matrix as two channels of a two-dimensional image, thereby forming a two-channel image. This conversion allows the terminal to perform a more in-depth analysis of the channel data, such as Fourier transform, to assess the data quality of the channel response matrix to be evaluated.

[0103] The terminal first extracts the channel response matrix to be evaluated, extracts the real part and imaginary part of each element in the channel response matrix to be evaluated, and obtains independent real part matrix and imaginary part matrix. Specifically, the element structure of the channel response matrix to be evaluated is The real part can be and the imaginary part Extract and get the real part matrix A as:

[0104]

[0105] The imaginary matrix B is:

[0106]

[0107] Step 304: Generate a first image corresponding to the channel response matrix to be evaluated according to the real matrix and the imaginary matrix.

[0108] In the embodiment of the present application, the terminal uses the real matrix A and the imaginary matrix B as the two channels of the first image to form a matrix of size The image data is the first image corresponding to the channel response matrix to be evaluated. The two channels of the first image are:

[0109]

[0110] Among them, each pixel position of the first image Contains two channels (component) is the real part of the channel response matrix, is the imaginary part of the channel response matrix.

[0111] In a specific embodiment, the real matrix Each element of the matrix maps to the first channel of the first image (for example, the brightness value of a grayscale image), and the imaginary part of the matrix Mapped to the second channel of the first image (for example, the contrast value of a grayscale image or the hue value of a pseudocolor image). If the first image generated from the real and imaginary matrices is a grayscale image, the grayscale values ​​can be obtained by weighting the real and imaginary matrices, or by mapping the values ​​based on amplitude. If the first image generated from the real and imaginary matrices is a color image, the elements of the real and imaginary matrices can be used as the values ​​of a specific color channel.

[0112] In this embodiment, by converting the high-dimensional channel response matrix to be evaluated into a first image in image form, the channel response matrix to be evaluated can be evaluated using image processing and signal processing techniques, which greatly improves the efficiency of data quality assessment and is suitable for real-time and large-scale data processing.

[0113] In an exemplary embodiment, Figure 4 As shown, step 104 includes steps 402 to 404. Among them:

[0114] Step 402 : Perform Fourier transform on the real matrix and the imaginary matrix of the first image respectively to obtain a first spectrum matrix and a second spectrum matrix.

[0115] In this embodiment of the present application, to further analyze the frequency characteristics contained in the channel response matrix, the terminal performs a two-dimensional Fourier transform on the two-channel image (first image) obtained by transforming the real and imaginary matrices. The Fourier transform is a tool used to convert data from the time or spatial domain to the frequency domain, thereby revealing hidden features such as periodicity, correlation, and randomness in the data. In the fields of signal processing and image processing, the Fourier transform is widely used for spectrum analysis, filtering, and feature extraction.

[0116] The terminal performs a two-dimensional Fourier transform on each channel in the first image to obtain a frequency domain representation. First, the terminal performs a Fourier transform on the information of the two channels in the first image, where the Fourier transform operation of the real part matrix A is:

[0117]

[0118] The Fourier transform operation of the imaginary matrix B is:

[0119]

[0120] in, , , is an imaginary unit, , is a complex number representing a phase factor; is the first spectrum matrix corresponding to the real part matrix, is the second spectrum matrix corresponding to the imaginary part matrix.

[0121] Through Fourier transform, the terminal can analyze the frequency characteristics of the image. For example, the low-frequency component represents the smooth part of the first image, and the high-frequency component represents the details and edge parts of the first image.

[0122] Step 404: construct a target spectrum matrix according to the first spectrum matrix and the second spectrum matrix.

[0123] In the embodiment of the present application, the terminal combines the first spectrum matrix and the second spectrum matrix to obtain a comprehensive target spectrum matrix, wherein the first spectrum matrix is , the second spectrum matrix is , then the target spectrum matrix is:

[0124]

[0125] Through Fourier transforms, the channel response matrix to be evaluated in the spatial domain (or time domain) can be converted to the frequency domain, resulting in a spectrum matrix. Frequency domain analysis can reveal the frequency characteristics of the channel, such as frequency-selective fading and the spectral distribution of interference. In the time domain, signals are often complex waveforms, making it difficult to visually observe their frequency components. In contrast, in the frequency domain, the frequency components of a signal are displayed as a histogram or spectrogram, allowing for intuitive visualization of the amplitude and phase of each frequency component. Therefore, frequency domain analysis can more easily detect periodic interference or periodic fading in the channel. The amplitude distribution of the spectrum matrix directly reflects the distribution of signal energy across different frequencies, facilitating channel quality assessment.

[0126] In this embodiment, the real matrix and the imaginary matrix are Fourier transformed respectively to obtain a first spectrum matrix and a second spectrum matrix. First, noise can be effectively filtered out or specific frequency components can be retained. Second, the channel response matrix to be evaluated is converted to the frequency domain for analysis, and multiple key features of the signal can be extracted, such as energy distribution, frequency selective fading, harmonic components, etc. These features may not be obvious in the time domain, but can be intuitively analyzed and evaluated in the frequency domain, and the operation of evaluating the channel response matrix to be evaluated is simplified, thereby improving the efficiency of data quality assessment.

[0127] In an exemplary embodiment, Figure 5 As shown, step 402 includes steps 502 to 508. Among them:

[0128] Step 502 : Initialize the real matrix and the imaginary matrix of the first image respectively to obtain a first initial matrix and a second initial matrix.

[0129] In the embodiment of the present application, the terminal initializes each element in the first image through the real matrix and the imaginary matrix of the first image, that is, creates a matrix of size The first initial matrix and the second initial matrix have initial values ​​of 0.

[0130] Step 504: For each spatial index, calculate a first phase factor corresponding to the real matrix and a second phase factor corresponding to the imaginary matrix.

[0131] In the embodiment of the present application, the terminal traverses each spatial domain of the first image, and for each spatial index of the first image , that is, from 1 to and from 1 to , calculate the first phase factor of the real part matrix at each spatial index and the second phase factor of the second spatial index.

[0132] In an exemplary embodiment, =4, =4, the spatial index is For example, the values ​​of the first phase factor and the second phase factor are .

[0133] Step 506 : Obtain a first weighted pixel value according to the first phase factor of each spatial index, and obtain a second weighted pixel value according to the second phase factor of each spatial index.

[0134] In an embodiment of the present application, the terminal calculates a first weighted pixel value based on the first phase factor corresponding to each spatial index contained in the first image and the element corresponding to the spatial index in the real matrix, and calculates a second weighted pixel value based on the second phase factor corresponding to each spatial index contained in the first image and the element corresponding to the spatial index in the real matrix, that is, the first weighted pixel value is the product of the real matrix and the first phase factor, and the second weighted pixel value is the product of the imaginary matrix and the second phase factor. The first weighted pixel value and the second weighted pixel value, as a component of the target spectrum matrix, are the accumulated parameters of the summation part of the Fourier transform process, reflecting the contribution of the pixel in the frequency domain.

[0135] Step 508: Accumulate the first weighted pixel values ​​according to the first initial matrix to obtain a first spectrum matrix, and accumulate the second weighted pixel values ​​according to the second initial matrix to obtain a second spectrum matrix.

[0136] In an embodiment of the present application, the terminal accumulates the first weighted pixel value of each spatial index into the first initial matrix to finally obtain a first spectrum matrix. At the same time, the terminal accumulates the second weighted pixel value of each spatial index into the second initial matrix to finally obtain a second spectrum matrix.

[0137] In this embodiment, the real matrix and the imaginary matrix are Fourier transformed respectively to obtain a first spectrum matrix and a second spectrum matrix. First, noise can be effectively filtered out or specific frequency components can be retained. Second, the channel response matrix to be evaluated is converted to the frequency domain for analysis, and multiple key features of the signal can be extracted, such as energy distribution, frequency selective fading, harmonic components, etc. These features may not be obvious in the time domain, but can be intuitively analyzed and evaluated in the frequency domain, and the operation of evaluating the channel response matrix to be evaluated is simplified, thereby improving the efficiency of data quality assessment.

[0138] In an exemplary embodiment, Figure 6 As shown, step 106 includes steps 602 to 606. Among them:

[0139] Step 602 : Calculate a first probability density distribution of the target spectrum matrix and a second probability density distribution of the standard spectrum matrix based on the target spectrum matrix and the standard spectrum matrix.

[0140] In this embodiment of the present application, after converting the channel response matrix to be evaluated into a target spectrum matrix via Fourier transform, the terminal analyzes the spectral characteristics of the target spectrum matrix to determine the data quality of the channel response matrix to be evaluated. In wireless communication systems, the spectral characteristics of a channel can reveal the complexity, regularity, and the effects of noise or interference in signal transmission. An ideal channel response typically exhibits a certain frequency distribution pattern, with its spectrum exhibiting relatively smooth attenuation characteristics and low noise components. Therefore, the complexity of the spectrum and its degree of conformity to theoretical models can serve as important criteria for measuring data quality. Based on the statistical characteristics of the channel (for example, the spectral entropy corresponding to the target spectrum matrix) and a known theoretical model (standard spectrum matrix), the terminal compares the spectral distribution with the ideal state to determine data quality. If the channel spectral distribution conforms to expected patterns or exhibits high complexity, it generally indicates good channel quality and reliable signal transmission. Conversely, if the spectral distribution is relatively simple or deviates from the theoretical model, it may indicate that the channel has significant attenuation, interference, or noise, which in turn affects data quality.

[0141] To evaluate the channel response matrix to be evaluated, the terminal uses the first spectrum entropy of the target spectrum matrix and the second spectrum entropy of the standard spectrum matrix as evaluation parameters. First, the terminal calculates the amplitude (modulus) of the target spectrum matrix:

[0142]

[0143] Then, the terminal normalizes the spectrum amplitude to obtain the first probability density distribution of the target spectrum matrix At the same time, the terminal calculates and obtains the second probability density distribution of the standard spectrum matrix according to the same principle.

[0144] Step 604 : Calculate a first spectrum entropy corresponding to the first probability density distribution, and calculate a second spectrum entropy corresponding to the second probability density distribution.

[0145] In the embodiment of the present application, the terminal calculates the first spectrum entropy H corresponding to the target spectrum matrix according to the first probability density distribution:

[0146]

[0147] Similarly, the terminal calculates the second spectrum entropy corresponding to the standard spectrum matrix according to the second probability density distribution .

[0148] Step 606: Determine a channel quality assessment result according to the first spectrum entropy and the second spectrum entropy.

[0149] In the embodiment of the present application, if the relationship between the first spectrum entropy and the second spectrum entropy is ≈ , indicating that the channel quality assessment result is that the data quality of the channel response matrix to be assessed is good; if the relationship between the first spectrum entropy and the second spectrum entropy is , it means that the channel quality assessment result is that the data quality is average or poor.

[0150] In an optional embodiment, the terminal may further perform a chi-square test on the target spectrum matrix and the standard spectrum matrix, and perform data quality assessment based on the similarity of the correlation coefficients between the target spectrum matrix and the standard spectrum matrix. Specifically, if the terminal performs a chi-square test on the target spectrum matrix and the standard spectrum matrix, the chi-square test formula is:

[0151]

[0152] in, is the observed value of the kth frequency component in the actual target spectrum matrix, is the corresponding expected value in the theoretical spectrum of the standard spectrum matrix.

[0153] If the terminal evaluates the similarity of the correlation coefficient between the target spectrum matrix and the standard spectrum matrix, the correlation coefficient calculation formula is:

[0154]

[0155] in, is the second probability density distribution corresponding to the theoretical spectrum in the standard spectrum matrix, and They represent the actual spectrum average in the target spectrum matrix and the average of the theoretical spectrum in the standard spectrum matrix respectively.

[0156] In this embodiment, a comparison framework between the actual spectrum and the theoretical spectrum is constructed, and the channel quality assessment result is determined using the first spectrum entropy and the second spectrum entropy. This provides a method for quantifying spectrum complexity and randomness, and then constructs a unified quality grading standard based on unified indicators. This not only reduces the difficulty of analyzing complex data, but also improves the efficiency and accuracy of channel quality assessment.

[0157] In an exemplary embodiment, Figure 7 As shown, an example of an object analysis method is provided, wherein:

[0158] Step 701: Obtain a channel response matrix to be evaluated of actual data and a standard spectrum matrix for theoretical channel characteristic analysis.

[0159] Step 702: Generate a first image according to the channel response matrix to be evaluated, and generate a second image according to the standard spectrum matrix.

[0160] In step 703, Fourier transform is performed on the first image and the second image respectively to obtain a target standard spectrum matrix and a standard spectrum matrix. Chi-square test, correlation coefficient and spectrum entropy calculation are performed on the target standard spectrum matrix and the standard spectrum matrix to obtain spectrum analysis results as evaluation results of the channel response matrix to be evaluated.

[0161] It should be understood that, although the various steps in the flowcharts involved in the various embodiments described above are displayed in sequence according to the instructions of the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the flowcharts involved in the various embodiments described above can include multiple steps or multiple stages, and these steps or stages are not necessarily executed and completed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of steps or stages in other steps.

[0162] Based on the same inventive concept, embodiments of the present application also provide an object assessment device for implementing the aforementioned object assessment method. The implementation solution provided by this device is similar to the implementation solution described in the aforementioned method. Therefore, the specific limitations of one or more of the following embodiments of the object assessment device can be found in the above-mentioned limitations of the object assessment method and will not be repeated here.

[0163] In an exemplary embodiment, Figure 8 As shown, an object evaluation device 800 is provided, comprising: a first acquisition module 801, a first frequency domain conversion module 802 and an evaluation module 803, wherein:

[0164] The first acquisition module 801 is used to obtain a channel response matrix to be evaluated and a standard spectrum matrix;

[0165] A first frequency domain conversion module 802 is configured to convert the channel response matrix to be evaluated into a first image, and perform frequency domain conversion on the first image to obtain a target spectrum matrix;

[0166] The evaluation module 803 is configured to determine a channel quality evaluation result based on the target spectrum matrix and the standard spectrum matrix.

[0167] In one embodiment, the apparatus 800 further includes:

[0168] A second acquisition module is used to obtain a standard channel response matrix;

[0169] The second frequency domain conversion module is used to convert the standard channel response matrix into a second image, and perform Fourier transform on the second image to obtain a standard frequency spectrum matrix.

[0170] In one embodiment, the first frequency domain conversion module 802 is specifically configured to determine a real matrix and an imaginary matrix corresponding to the channel response matrix to be evaluated;

[0171] A first image corresponding to the channel response matrix to be evaluated is generated according to the real matrix and the imaginary matrix.

[0172] In one embodiment, the first frequency domain conversion module 802 is specifically configured to perform Fourier transform on the real matrix and the imaginary matrix of the first image respectively to obtain a first spectrum matrix and a second spectrum matrix;

[0173] A target spectrum matrix is ​​constructed according to the first spectrum matrix and the second spectrum matrix.

[0174] In one embodiment, the first frequency domain conversion module 802 is specifically configured to initialize the real matrix and the imaginary matrix of the first image respectively to obtain a first initial matrix and a second initial matrix;

[0175] For each spatial index, calculating a first phase factor corresponding to the real matrix and a second phase factor corresponding to the imaginary matrix;

[0176] Obtaining a first weighted pixel value according to a first phase factor of each spatial index, and obtaining a second weighted pixel value according to a second phase factor of each spatial index;

[0177] The first weighted pixel values ​​are accumulated according to the first initial matrix to obtain a first spectrum matrix, and the second weighted pixel values ​​are accumulated according to the second initial matrix to obtain a second spectrum matrix.

[0178] In one embodiment, the evaluation module 803 is specifically configured to calculate a first probability density distribution of the target spectrum matrix and a second probability density distribution of the standard spectrum matrix based on the target spectrum matrix and the standard spectrum matrix respectively;

[0179] Calculating a first spectrum entropy corresponding to the first probability density distribution, and calculating a second spectrum entropy corresponding to the second probability density distribution;

[0180] A channel quality assessment result is determined according to the first spectrum entropy and the second spectrum entropy.

[0181] Each module in the object assessment device described above may be implemented in whole or in part through software, hardware, or a combination thereof. Each module may be embedded in or independent of a processor in a computer device in the form of hardware, or may be stored in a memory in the computer device in the form of software, so that the processor can call and execute the corresponding operations of each module.

[0182] In an exemplary embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as shown in FIG. Figure 9 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O) and a communication interface. The processor, memory and input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The database of the computer device is used to store standard spectrum matrix data. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, an object evaluation method is implemented.

[0183] Those skilled in the art will understand that Figure 9 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0184] In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the following steps are implemented:

[0185] Obtaining a channel response matrix to be evaluated and a standard spectrum matrix;

[0186] Converting the channel response matrix to be evaluated into a first image, and performing frequency domain conversion on the first image to obtain a target spectrum matrix;

[0187] A channel quality assessment result is determined based on the target spectrum matrix and the standard spectrum matrix.

[0188] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0189] Obtain a standard channel response matrix;

[0190] The standard channel response matrix is ​​converted into a second image, and the second image is Fourier transformed to obtain a standard spectrum matrix.

[0191] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0192] Determine the real matrix and the imaginary matrix corresponding to the channel response matrix to be evaluated;

[0193] A first image corresponding to the channel response matrix to be evaluated is generated according to the real matrix and the imaginary matrix.

[0194] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0195] Performing Fourier transform on the real matrix and the imaginary matrix of the first image respectively to obtain a first spectrum matrix and a second spectrum matrix;

[0196] A target spectrum matrix is ​​constructed according to the first spectrum matrix and the second spectrum matrix.

[0197] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0198] Initializing the real matrix and the imaginary matrix of the first image respectively to obtain a first initial matrix and a second initial matrix;

[0199] For each spatial index, calculating a first phase factor corresponding to the real matrix and a second phase factor corresponding to the imaginary matrix;

[0200] Obtaining a first weighted pixel value according to a first phase factor of each spatial index, and obtaining a second weighted pixel value according to a second phase factor of each spatial index;

[0201] The first weighted pixel values ​​are accumulated according to the first initial matrix to obtain a first spectrum matrix, and the second weighted pixel values ​​are accumulated according to the second initial matrix to obtain a second spectrum matrix.

[0202] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0203] Calculate a first probability density distribution of the target spectrum matrix and a second probability density distribution of the standard spectrum matrix based on the target spectrum matrix and the standard spectrum matrix respectively;

[0204] Calculating a first spectrum entropy corresponding to the first probability density distribution, and calculating a second spectrum entropy corresponding to the second probability density distribution;

[0205] A channel quality assessment result is determined according to the first spectrum entropy and the second spectrum entropy.

[0206] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0207] In one embodiment, a computer program product is provided, including a computer program, which implements the steps in the above method embodiments when executed by a processor.

[0208] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0209] Those skilled in the art will understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. In particular, any reference to memory, database, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The databases involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the various embodiments provided herein may be, but are not limited to, general-purpose processors, central processing units (CPUs), graphics processing units (GPUs), digital signal processors (DSPs), programmable logic devices (PLDs), quantum computing-based data processing logic devices, artificial intelligence (AI) processors, and the like.

[0210] The technical features of the above embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0211] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present application. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present application, and these modifications and improvements fall within the scope of protection of the present application. Therefore, the scope of protection of the present application shall be determined by the appended claims.

Claims

1. An object evaluation method, characterized in that: The method comprises: Obtaining a channel response matrix to be evaluated and a standard spectrum matrix; Converting the channel response matrix to be evaluated into a first image, and performing frequency domain conversion on the first image to obtain a target spectrum matrix; A channel quality assessment result is determined based on the target spectrum matrix and the standard spectrum matrix.

2. The method according to claim 1, characterized in that Before obtaining the channel response matrix to be evaluated and the standard spectrum matrix, the method further includes: Obtain a standard channel response matrix; The standard channel response matrix is ​​converted into a second image, and the second image is subjected to Fourier transform to obtain a standard spectrum matrix.

3. The method according to claim 1, characterized in that The converting the channel response matrix to be evaluated into a first image comprises: Determine a real matrix and an imaginary matrix corresponding to the channel response matrix to be evaluated; A first image corresponding to the channel response matrix to be evaluated is generated according to the real matrix and the imaginary matrix.

4. The method according to claim 1, characterized in that: The performing frequency domain conversion on the first image to obtain a target spectrum matrix includes: Performing Fourier transform on the real matrix and the imaginary matrix of the first image respectively to obtain a first spectrum matrix and a second spectrum matrix; A target spectrum matrix is ​​constructed according to the first spectrum matrix and the second spectrum matrix.

5. The method according to claim 4, characterized in that The performing Fourier transform on the real matrix and the imaginary matrix of the first image respectively to obtain a first spectrum matrix and a second spectrum matrix comprises: Initializing the real matrix and the imaginary matrix of the first image respectively to obtain a first initial matrix and a second initial matrix; For each spatial index, calculating a first phase factor corresponding to the real matrix and a second phase factor corresponding to the imaginary matrix; Obtaining a first weighted pixel value according to the first phase factor of each of the spatial indexes, and obtaining a second weighted pixel value according to the second phase factor of each of the spatial indexes; The first weighted pixel values ​​are accumulated according to the first initial matrix to obtain a first spectrum matrix, and the second weighted pixel values ​​are accumulated according to the second initial matrix to obtain a second spectrum matrix.

6. The method according to claim 1, characterized in that The determining of the channel quality assessment result based on the target spectrum matrix and the standard spectrum matrix includes: Based on the target spectrum matrix and the standard spectrum matrix, respectively calculate a first probability density distribution of the target spectrum matrix and a second probability density distribution of the standard spectrum matrix; Calculating a first spectrum entropy corresponding to the first probability density distribution, and calculating a second spectrum entropy corresponding to the second probability density distribution; A channel quality assessment result is determined according to the first spectrum entropy and the second spectrum entropy.

7. An object evaluation device, characterized in that The device comprises: A first acquisition module is used to acquire a channel response matrix to be evaluated and a standard spectrum matrix; A first frequency domain conversion module, used for converting the channel response matrix to be evaluated into a first image, and performing frequency domain conversion on the first image to obtain a target spectrum matrix; An evaluation module is used to determine a channel quality evaluation result based on the target spectrum matrix and the standard spectrum matrix.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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