Computer memory bank aging test system

Abstract

The invention discloses a computer memory bank aging test system, and relates to the technical field of computer hardware test.The computer memory bank aging test system comprises the steps that grids are divided on the surface of a PCB, coordinate datum points are printed in a silk-screen mode, the temperature of each grid is collected in real time through a thermal imager, and an average temperature and smooth gray value sequence is constructed; an amplitude spectrum standard deviation of a smooth gray value is extracted through FFT transformation, ASI is calculated in combination with kurtosis and skewness, a thermodynamic diagram is generated according to the aging degrees of different grids, a three-stage ASI change rate sequence is analyzed based on an LSTM model, and therefore the life cycle stage and the aging type of the computer memory bank are judged. According to the invention, the aging evaluation precision of the memory bank is improved through multi-dimensional data fusion, the maintenance cost of the memory bank can be reduced, and the system reliability is improved.

Description

Technical Field

[0001] The invention relates to the technical field of computer hardware testing, in particular to a computer memory bar aging testing system. Background Art

[0002] With the continuous development of computer technology, the importance of computer memory in computer systems has become increasingly prominent. The aging degree of memory directly affects the performance and stability of the computer. However, there is currently a lack of an efficient and accurate method to directly detect the aging of the internal components of the memory. Most of the existing detection methods rely on complex disassembly and professional equipment, which are cumbersome to operate and may cause damage to the memory. Therefore, it is of great practical significance to develop a test system that can accurately evaluate the aging degree of memory in a non-invasive and indirect way. Summary of the invention

[0003] In view of the deficiencies of the prior art, the present invention provides a computer memory stick aging test system, which solves the problems of the prior art that the operation is cumbersome and the memory stick is easily damaged, and the aging degree of the memory stick cannot be evaluated non-invasively, accurately and in real time.

[0004] To achieve the above objectives, the present invention is implemented through the following technical solutions: A computer memory bar aging test system, comprising: A memory bar aging judgment module generates heat maps with the same grid division for the three stages of the memory bar life cycle, calculates the change rate of each grid in the three stages compared with the benchmark ASI, obtains the ASI change rate sequences A1i, A2i, and A3i of different grids, substitutes multiple groups of A1i, A2i, and A3i into the long short-term memory network for training, inputs the ASI change rate sequence of the untested memory bar into the trained model, judges the life cycle stage of the memory bar, calculates the proportion of green grids and red grids Green and Red according to the heat map, and judges the aging type in combination with the distribution of the red grid. The Green and Red values ​​are calculated by counting the number of all green grids and red grids in the heat map, and then dividing them by the total number of grids respectively. The three stages of the memory bar life cycle are the initial stage, the middle stage, and the final stage.

[0005] Before the memory module aging judgment module, there are also a grid positioning module, a grid temperature acquisition module, a grid aging calculation module, and a heat map generation module. The grid positioning module divides the surface of the outermost PCB board of the computer memory module into square grids with the same area, assigns a unique coordinate to each grid, silk-screens grid coordinate reference points on the edge of the PCB board, and transmits the divided grids with coordinates to the grid temperature acquisition module. The grid temperature acquisition module collects thermal imaging pictures within T minutes at a frequency of f1 through a thermal imager, statistically calculates the average temperature of each grid in each thermal imaging picture, combines all the pictures within T minutes to obtain the average temperature sequence corresponding to each grid, and transmits the average temperature sequence to the grid aging sensitivity index calculation module. The grid aging calculation module converts the average temperature sequence of each grid into a grayscale value sequence, performs smoothing processing on the grayscale value sequence to obtain Gt, constructs an aging sensitivity index ASI through the kurtosis, skewness, and amplitude spectrum standard deviation of Gt, and transmits the ASI of each grid to the heat map generation module. The heat map generation module maps it to different colors and grades according to the ASI value of the grid, generates a heat map using the coordinates corresponding to each colored grid, and transmits the heat map to the memory module life cycle and aging type judgment module.

[0006] As a further solution of the present invention, the average temperature sequence is converted into a grayscale value sequence according to the formula G=(T - a)×(255 - 0)÷(b - a), where a and b are respectively the minimum value and the maximum value of the average temperature sequence, and T∈[a,b].

[0007] As a further solution of the present invention, a fast Fourier transform is performed on Gt to convert the time-domain signal into a frequency-domain signal F(f), the amplitude spectrum |F(fhight)| with fhight>=flimit is extracted, and its standard deviation is calculated. flimit is the aging sensitivity frequency threshold.

[0008] As a further solution of the present invention, the aging degree of the grid is calculated according to the formula ASI = piu*((kurtosis(Gt)+skewness(Gt) / 2)), where piu is the amplitude spectrum standard deviation.

[0009] As a further solution of the present invention, based on the ASI distribution of multiple groups of samples with different aging degrees, the Otsu algorithm is used to automatically determine the thresholds Grade1, Grade2, and Grade3, and the aging grades of the grids are divided as follows: ASI<=Grade1, healthy, classified as grade 1, corresponding to green; Grade1<ASI<=Grade2, slightly aged, classified as grade 2, corresponding to blue; Grade2<ASI<=Grade3, moderately aged, classified as grade 3, corresponding to white; ASI>Grade3, severely aged, classified as grade 4, corresponding to red.

[0010] As a further solution of the present invention, the specific steps of generating a thermal map are: Map the ASI value of each grid to the corresponding color level; Bind each grid's coordinates to its corresponding color level; According to the silk-screen reference points on the edge of the PCB board, calibrate the grid coordinates with the actual physical position; Create a canvas that matches the size of the PCB board, traverse each grid, fill it with the corresponding color according to its aging level, and mark the grid coordinate reference points and scale lines.

[0011] As a further embodiment of the present invention, according to the formula Aqi=(ASIq-ASI 基准 ) / ASI 基准 Calculate the ASI change rate of the grid at different stages, where ASIq is the grid ASI of the current memory bank, and ASI 基准 Obtained for testing new memory sticks that have not aged.

[0012] As a further solution of the present invention, the specific steps of determining the aging type of the memory stick are: If Red<=th1 and Green>=gh2, the memory module is judged to be in a state of continuous and stable aging; If Red∈(th1,th2], Green∈[gh1,gh2) and the red grids are scattered, the memory bar is judged to be in intermittent aging; If Red∈(th1,th2], Green∈[0,gh1] and the red grids are clustered, the memory bar is judged to be in accelerated aging; If Red>th2, the memory bar is judged to be seriously aged and a mandatory warning is required, where th1, th2, gh1, and gh2 are percentage thresholds.

[0013] As a further solution of the present invention, the specific method for judging the aggregation and dispersion of red grids is: Taking the red grid as the center, for each red grid, count the 9 grids in the surrounding 3×3 neighborhood, including the red grid itself, count the number of red grids in these 9 grids, and then divide by 9 to get the red percentage of the neighborhood; If there is at least one neighborhood with a red ratio greater than or equal to 30%, the red grid is considered concentrated; If the red proportion of all neighborhoods is less than 30%, the red grid is judged to be scattered.

[0014] The present invention provides a computer memory bar aging test system. Compared with the prior art, it has the following beneficial effects: (1) The present invention adopts non-invasive thermal imaging technology, which can dynamically monitor the aging process of the memory stick in real time without disassembling it. The aging area can be located through fine grid division, and abnormal temperature fluctuations can be accurately captured, significantly improving the efficiency and accuracy of aging detection; (2) The present invention constructs an aging sensitivity index model by extracting the temporal and spatial characteristics of the grid temperature sequence, intuitively presents the aging distribution with the help of heat map visualization technology, intelligently determines the life cycle stage with the LSTM model, and collects the proportion of grids of different colors through heat maps to determine the aging stage of the memory stick in different life cycles, thereby providing quantitative data support for the full life cycle management of the memory stick. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 This is a block diagram of the system principle of the present invention. DETAILED DESCRIPTION

[0016] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0017] like Figure 1 The present invention provides a computer memory bar aging test system, comprising: Grid positioning module, this module divides the surface of the outermost PCB board of the computer memory bar into square grids of uniform area. Each grid has the same area, which is convenient for subsequent unified data collection and analysis of each area. The size of the divided grid can be adjusted according to the actual size of the PCB board and the test accuracy requirements. For example, for a DDR4 memory bar PCB board with a length of 133.35mm and a width of 31.75mm, we can divide it into square grids with a side length of 5mm. It can be divided into approximately 133.35÷5≈27 grids in the length direction and approximately 31.75÷5≈6 grids in the width direction, with a total of approximately 27×6=162 grids; Assign unique coordinates (x, y) to each grid. The coordinates are set in the Cartesian coordinate system. The upper left corner of the PCB board is the origin (0, 0). The horizontal right direction is the positive direction of the x-axis, and the vertical downward direction is the positive direction of the y-axis. For example, in the above-divided grid, the coordinates of the first grid in the first row are (0, 0), the coordinates of the second grid in the first row are (1, 0), and the coordinates of the first grid in the second row are (0, 1). And so on. Each grid can be accurately identified by a unique coordinate, which is convenient for subsequent data processing and positioning. Grid coordinate reference points are printed on the edge of the PCB board to help operators quickly locate each grid in actual operation. For example, we can print (0,0) in the upper left corner, (26,0) in the upper right corner, (0,5) in the lower left corner, and (26,5) in the lower right corner of the PCB board. When viewing thermal imaging images or performing data analysis, operators can quickly find the corresponding grid positions based on these reference points.

[0018] Grid temperature acquisition module: This module uses a thermal imager to collect thermal imaging pictures within T minutes at a frequency of f1. The acquisition frequency f1 and the acquisition duration T need to be set according to specific test requirements. For example, for a general memory bar aging test, we can set the acquisition frequency f1 to 5Hz, which means that 5 thermal imaging pictures are collected per second. The acquisition duration T can be set to 10 minutes, that is, 600 seconds. In these 10 minutes, the thermal imager will collect a total of 600×5=3000 thermal imaging pictures; Collect the average temperature of each grid in each thermal imaging image. The images collected by the thermal imager contain the temperature distribution information of the PCB surface. We can use image processing and data analysis technology to extract the temperature value in each grid and calculate its average value. For example, for a grid in a thermal imaging image, there may be multiple pixels inside it, and each pixel corresponds to a temperature value. We can add the temperature values ​​of these pixels and divide them by the number of pixels to get the average temperature of the grid. By collecting the average temperature of each grid in 3000 thermal imaging images, we can get the average temperature sequence A1, A2, ..., A3000 of each grid within 10 minutes. Assuming that the average temperature of a grid in the first second is 32℃, the average temperature in the second second is 32.5℃, and so on, a temperature sequence [32, 32.5, ...] is formed.

[0019] Grid aging calculation module, which converts the average temperature sequence of each grid into a gray value sequence. The conversion can be performed using a linear mapping method, that is, converting the values ​​in the average temperature sequence into a range of 0-255. This step is to facilitate subsequent signal processing and analysis. For example, assuming that the average temperature sequence of a grid is [20°C,...,60°C], 20°C needs to be mapped to a gray value of 0 and 60°C needs to be mapped to a gray value of 255. For a temperature value T of the grid, its corresponding gray value G can be calculated using the following formula: G=(T-20)×(255-0)÷(60-20). If T=30°C, then G=(30-20)×(255-0)÷(60-20)=63.75, which is rounded to 64. The average temperature sequence is divided into sliding windows of t seconds, and the grayscale average of each window is calculated to obtain the smoothed grayscale sequence G1,...,Gm. The size of the sliding window t needs to be adjusted according to the actual situation. Generally, 5-10 seconds are selected. For example, we choose a sliding window of t=5 seconds. For the 3000 average temperature data points collected earlier, they correspond to 3000 grayscale values. With 5 seconds as a window, each window contains 5×5=25 grayscale values. We calculate the average of the 25 grayscale values ​​in each window and use it as the grayscale average corresponding to the window. The grayscale average of the first window is recorded as G1, and the grayscale average of the second window is recorded as G2, and so on, until the grayscale average of all windows is obtained to form a smoothed grayscale sequence; Perform FFT (Fast Fourier Transform) on the smoothed grayscale sequence Gt to convert the time domain signal into the frequency domain signal F(f). FFT can decompose the signal into components of different frequencies, which is convenient for us to analyze the frequency characteristics of the signal. Define the aging sensitive frequency range fhight>=flimit. Here, flimit is a threshold determined through a large number of experiments and data analysis. For example, through the test and analysis of multiple groups of aging memory sticks, we determined that the aging sensitive frequency range is 15Hz and above, that is, flimit=15. Extract the amplitude spectrum |F(fhight)| of this segment and calculate its standard deviation piu. The standard deviation reflects the discreteness of the data. The larger the piu value, the greater the amplitude fluctuation in the frequency range, which may indicate that the aging of the grid is more serious. The kurtosis and skewness of Gt are obtained. The kurtosis describes the peak degree of the data distribution, and the skewness describes the asymmetry of the data distribution. If the kurtosis of the grayscale sequence of a certain grid is large, it means that the data distribution is relatively peaked, and there may be some abnormal temperature fluctuations; if the skewness is not 0, it means that the data distribution is asymmetric, which may imply the unevenness of the aging process; Using only the standard deviation of the amplitude spectrum may not fully capture the aging characteristics. For example, if two grids have the same standard deviation, but one has a high kurtosis, which means more extreme fluctuations, and the other has a low kurtosis, which means uniform fluctuations, the former is more likely to be in an aging state. The skewness can reflect the long-term trend of temperature change, such as gradual increase, which cannot be reflected by the standard deviation. Therefore, starting from the time domain and frequency domain of Gt, the kurtosis, skewness, and amplitude spectrum standard deviation of Gt are combined to construct the aging sensitivity index ASI. The specific formula is: ASI=piu*((kurtosis(Gt)+skewness(Gt) / 2)). Through this formula, an index that can reflect the degree of grid aging is obtained. For example, if the piu of a grid is 0.8, the kurtosis(Gt)=4, and the skewness(Gt)=1, then the ASI of the grid is 0.8×((4+1 / 2))=3.6; Based on the ASI distribution of multiple groups of samples with different aging degrees, the Otsu algorithm is used to automatically determine the threshold. The Otsu algorithm is a commonly used image segmentation algorithm. It can automatically find an optimal threshold according to the data distribution and divide the data into two categories. For example, by analyzing 200 groups of memory module samples with different aging degrees, the Otsu algorithm determines that Grade1 = 1.5, Grade2 = 2.5, Grade3 = 3.5. According to these thresholds, we can divide the aging level of the grid into: ASI <= Grade1, healthy; Grade1 < ASI <= Grade2, mildly aged; Grade2 < ASI <= Grade3, moderately aged; ASI > Grade3, severely aged.

[0020] The heat map generation module divides the grid into 4 levels according to the ASI value. ASI <= Grade1 is classified as level 1, corresponding to green; Grade1 < ASI <= Grade2 is classified as level 2, corresponding to blue; Grade2 < ASI <= Grade3 is classified as level 3, corresponding to white; ASI > Grade3 is classified as level 4, corresponding to red. The selected color correspondence method is based on people's intuitive perception of colors. Green usually represents healthy and normal, and red represents dangerous and severe. For example, for the grid with ASI = 3.6 calculated previously, since 3.6 > 3.5, this grid belongs to level 4 and corresponds to red. Map the ASI value of each grid to the corresponding color level; bind the coordinates (x, y) of each grid to its corresponding color level to form structured data, such as a dictionary or a two-dimensional array; calibrate the virtual grid coordinates with the actual physical position according to the silk screen reference points on the edge of the PCB board; draw grid lines to ensure alignment with the circuit layout of the PCB board; create a canvas that matches the size of the PCB board, traverse each grid, fill it with the corresponding color according to its aging level, and mark the grid coordinate reference points and scale lines.

[0021] The memory module aging judgment module divides the life cycle of the memory module into three stages: the initial stage, the middle stage, and the end stage. In each stage, we generate an aging heat map with the same grid division according to the previous method and calculate the change rate of each grid compared to the reference ASI in the three stages. For example, taking one of the grids as an example, the reference ASI is obtained by testing a certain grid of new memory modules of the same batch. 基准is 0.6. In the initial stage, the ASI of a certain grid is 1; in the middle stage, the ASI of the grid becomes 1.8; in the final stage, the ASI of the grid becomes 3. Then the relative ASI change rate of the grid in the initial stage relative to the benchmark is (1-0.6) / 0.6≈0.67, the relative ASI change rate in the middle stage relative to the benchmark is (1.8-0.6) / 0.6=2, and the relative ASI change rate in the final stage relative to the benchmark is (3-0.6) / 0.6=4. By performing such calculations on all grids, we obtain the ASI change rate sequence A1i, A2i, A3i in each stage, i∈[1,M], where M is the total number of thermal maps obtained in each stage; Multiple groups of A1i, A2i, and A3i sequences at different stages are substituted into the LSTM (long short-term memory) network for training. LSTM is a special recurrent neural network that can process sequence data and has memory function. It is suitable for analyzing time series information during the aging process of memory bars. For example, we can use deep learning frameworks such as TensorFlow or PyTorch to build and train LSTM models. During the training process, we use the memory bar data at known life cycle stages as the training set and adjust the model parameters so that the model can accurately determine the life cycle stage of the memory bar based on the ASI change rate sequence. The trained model can predict the new memory bar data and determine its current life cycle stage. After determining the current life cycle stage of the memory stick, calculate the proportion of green and red grids at this moment according to the heat map, count the number of all green grids and the number of red grids, and then divide them by the total number of grids to get the values ​​of Green and Red. According to the values ​​of Green and Red and the distribution of red grids, determine the aging type; If Red <= 5% and Green >= 60%, it is considered to be in continuous stable aging, which means that most areas of the memory stick are in a healthy state, and only a few areas are slightly aged. The aging process is slow and stable; If Red∈[5%,15%], Green∈[40%,60%] and the red grids are scattered, it is judged to be in intermittent aging, which means that a certain proportion of the memory bar has aged, but the aging area is relatively scattered, which may be due to some accidental factors causing local aging; If Red∈[5%,15%], Green∈[0,40%] and the red grids are clustered, it is judged to be in accelerated aging. At this time, the aging areas not only have a certain proportion but are also clustered together, indicating that the aging process is accelerating, which may have a significant impact on the performance of the memory stick; If Red>15%, a mandatory warning is issued, which indicates that the memory module is seriously aged and may fail at any time, and needs to be replaced or repaired in time; The method to judge the aggregation and dispersion of red grids is: take each red grid as the center and calculate the red proportion in the 3×3 neighborhood around it. For example, for a certain red grid, its surrounding 3×3 neighborhood contains 9 grids, including the red grid itself. Count the number of red grids in these 9 grids, and then divide it by 9 to get the red proportion of the neighborhood. If there is at least one neighborhood with a red proportion greater than or equal to 30%, the red grid is judged to be concentrated. If the red proportion of all neighborhoods is less than 30%, the red grid is judged to be dispersed. Through this method, we can accurately judge the distribution of red grids, and thus more accurately judge the aging type.

[0022] Some of the data in the above formulas are dimensionless and numerically calculated. Meanwhile, the contents not described in detail in this specification belong to the prior art known to those skilled in the art.

[0023] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A computer memory bar aging test system, characterized in that: include: A memory bar aging judgment module generates heat maps with the same grid division for the three stages of the memory bar life cycle, calculates the change rate of each grid in the three stages compared with the benchmark ASI, obtains the ASI change rate sequences A1i, A2i, and A3i of different grids, substitutes multiple groups of A1i, A2i, and A3i into the long short-term memory network for training, inputs the ASI change rate sequence of the untested memory bar into the trained model, judges the life cycle stage of the memory bar, calculates the proportion of green grids and red grids Green and Red according to the heat map, and judges the aging type in combination with the distribution of the red grid. The Green and Red values ​​are calculated by counting the number of all green grids and red grids in the heat map, and then dividing them by the total number of grids respectively. The three stages of the memory bar life cycle are the initial stage, the middle stage, and the final stage.

2. A computer memory bar aging test system according to claim 1, characterized in that: The memory bar aging judgment module also includes a grid positioning module, a grid temperature collection module, a grid aging calculation module, and a thermal map generation module. The grid positioning module divides the surface of the outermost PCB board of the computer memory bar into square grids with uniform areas, assigns unique coordinates to each grid, prints grid coordinate reference points on the edge of the PCB board, and transmits the divided coordinate grids to the grid temperature collection module. The grid temperature collection module uses a thermal imager to collect thermal imaging pictures within T minutes at a frequency of f1, calculates the average temperature of each grid in each thermal imaging picture, and combines all pictures within T minutes to obtain the average temperature of each grid. The average temperature sequence corresponding to the grid is obtained, and the average temperature sequence is transmitted to the grid aging sensitivity index calculation module, the grid aging calculation module converts the average temperature sequence of each grid into a gray value sequence, smoothes the gray value sequence to obtain Gt, and constructs an aging sensitivity index ASI through the kurtosis, skewness, and amplitude spectrum standard deviation of Gt, and transmits the ASI of each grid to the heat map generation module, the heat map generation module maps the grid to different colors and levels according to the ASI value of the grid, uses the coordinates corresponding to each color grid to generate a heat map, and transmits the heat map to the memory bar life cycle and aging type judgment module.

3. A computer memory bar aging test system according to claim 2, characterized in that: The average temperature sequence is converted into a gray value sequence according to the formula G=(Ta)×(255-0)÷(ba), where a and b are the minimum and maximum values ​​of the average temperature sequence, respectively, T∈[a,b].

4. A computer memory bar aging test system according to claim 2, characterized in that: Perform fast Fourier transform on Gt, convert the time domain signal into the frequency domain signal F(f), extract the amplitude spectrum |F(fhight)| with fhight>=flimit, and calculate its standard deviation. flimit is the aging sensitive frequency threshold.

5. A computer memory bar aging test system according to claim 4, characterized in that: The aging degree of the grid is calculated according to the formula ASI=piu*((kurtosis(Gt)+skewness(Gt) / 2)), where piu is the standard deviation of the amplitude spectrum.

6. A computer memory bar aging test system according to claim 2, characterized in that: Based on the ASI distribution of multiple groups of samples with different aging degrees, the Otsu algorithm is used to automatically determine the thresholds Grade1, Grade2, and Grade3, and the aging grades of the grids are classified as follows: ASI <= Grade1, healthy, classified as Grade 1, corresponding to green; Grade1 < ASI <= Grade2, mildly aged, classified as Grade 2, corresponding to blue; Grade2 < ASI <= Grade3, moderately aged, classified as Grade 3, corresponding to white; ASI > Grade3, severely aged, classified as Grade 4, corresponding to red.

7. A computer memory bar aging test system according to claim 2, characterized in that: The specific steps to generate the heat map are as follows: Map the ASI value of each grid to the corresponding color grade; Bind the coordinates of each grid to its corresponding color grade; Calibrate the grid coordinates with the actual physical position according to the silk screen reference points on the edge of the PCB board; Create a canvas that matches the size of the PCB board, traverse each grid, fill in the corresponding color according to its aging grade, and mark the grid coordinate reference points and scale lines.

8. A computer memory bar aging test system according to claim 1, characterized in that: According to the formula Aqi=(ASIq-ASI 基准 ) / ASI 基准 Calculate the ASI change rate of the grid at different stages, where ASIq is the grid ASI of the current memory bank, and ASI 基准 Obtained for testing new memory sticks that have not aged.

9. A computer memory bar aging test system according to claim 1, characterized in that: The specific steps to determine the aging type of the memory module are as follows: If Red <= th1 and Green >= gh2, it is determined that the memory module is in continuous stable aging; If Red ∈ (th1, th2], Green ∈ [gh1, gh2) and the red grids are dispersed, it is determined that the memory module is in intermittent aging; If Red ∈ (th1, th2], Green ∈ [0, gh1) and the red grids are aggregated, it is determined that the memory module is in accelerated aging; If Red > th2, it is determined that the memory module is severely aged and a forced warning is required, where th1, th2, gh1, and gh2 are ratio thresholds.

10. A computer memory bar aging test system according to claim 9, characterized in that: The specific method to determine the aggregation and dispersion of the red grids is as follows: Centered on the red grids, for each red grid, count the 9 grids in the surrounding 3×3 neighborhood, including the red grid itself, count the number of red grids in these 9 grids, and then divide by 9 to obtain the red ratio of this neighborhood; If there is at least one neighborhood with a red ratio greater than or equal to 30%, it is determined that the red grids are concentrated; If the red ratio of all neighborhoods is less than 30%, it is determined that the red grids are dispersed.

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