Method, device and system for detecting particle size performance

Abstract

The application provides a particle size performance detection method, a detection device and a detection system. The detection method comprises the following steps: performing particle size testing on a sample to obtain particle size data of particles in the sample; dividing a plurality of particle size distribution intervals according to the particle size data and calculating probability density values of the particle size distribution intervals; generating a density distribution feature map according to the probability density values and the particle size distribution intervals, wherein the density distribution feature map comprises at least one figure group, one figure group comprises a plurality of sequentially connected figures, the figure group and the sample are in one-to-one correspondence, the maximum width of one figure corresponds to one particle size distribution interval of the sample, and the figure attribute of one figure corresponds to the probability density value of one particle size distribution interval; and determining the particle size performance of the sample according to the density distribution feature map. The density distribution feature map generated by the detection method avoids superposition between density distribution feature maps of different samples, and facilitates determination of the particle size performance of the sample according to read data.

Description

Technical Field

[0001] This application relates to the field of particle size evaluation technology, and more specifically, to a method, apparatus, computer-readable storage medium, processor, and detection system for detecting particle size performance. Background Technology

[0002] Currently, grain size evaluation of clastic rocks typically uses conventional charts, such as frequency curves or histograms, to characterize their grain size distribution. However, due to the uneven grain size distribution range, conventional charts cannot accurately represent the distribution, and sometimes the represented distribution characteristics are misleading. Secondly, it is difficult to distinguish between multiple overlaid curves, thus limiting the comparison of grain size among different samples.

[0003] The information disclosed above in the background section is only intended to enhance the understanding of the background art of the art described herein. Therefore, the background art may contain certain information that does not constitute prior art known to those skilled in the art in this country. Summary of the Invention

[0004] The main objective of this application is to provide a method, apparatus, computer-readable storage medium, processor, and detection system for detecting particle size properties, in order to solve the problem of overlapping density distribution feature maps of different samples in the prior art.

[0005] To achieve the above objectives, according to one aspect of this application, a method for detecting particle size performance is provided, comprising: performing a particle size test on a sample to obtain particle size data of the particles in the sample; dividing the particle size data into multiple particle size distribution intervals, calculating a probability density value for each particle size distribution interval, wherein the probability density value is a percentage of a first quantity to a second quantity, the first quantity being the number of particles located within the particle size distribution interval, and the second quantity being the total number of particles in the sample; generating a density distribution feature map based on the probability density value and the particle size distribution intervals, wherein the density distribution feature map includes at least one graphic group, a graphic group including multiple sequentially connected graphics, the graphic group corresponding one-to-one with the sample, the maximum width of a graphic corresponding to one of the particle size distribution intervals of the sample, and the graphic attribute of a graphic corresponding to the probability density value of one of the particle size distribution intervals; and determining the particle size performance of the sample based on the density distribution feature map.

[0006] Optionally, the graphic attributes include color gradient, dot density, line width, line height, and line density.

[0007] Optionally, dividing the granularity data into multiple granularity distribution intervals and calculating the probability density value of each granularity distribution interval includes: determining a total granularity distribution interval based on the maximum and minimum values ​​of the granularity data; dividing the total granularity distribution interval into multiple preliminary granularity distribution intervals and calculating the probability density value of each preliminary granularity distribution interval; if the probability density value of a preliminary granularity distribution interval is greater than or equal to a predetermined value, dividing the preliminary granularity distribution interval into two preliminary granularity distribution intervals, until the probability density value of any one of the preliminary granularity distribution intervals is less than the predetermined value, thereby obtaining multiple granularity distribution intervals.

[0008] Optionally, determining the particle size properties of the sample based on the density distribution feature map includes: obtaining the particle size parameters of the sample based on the density distribution feature map; calculating the particle size distribution parameters based on the particle size parameters; and determining the sorting grade of the sample based on the particle size distribution parameters.

[0009] Optionally, the particle size parameters include initial particle size, minimum particle size, average particle size, median particle size, content of different particle sizes, probability density value, and PHI value of each cumulative probability.

[0010] Optionally, the distribution characteristic parameters include sorting coefficient, standard deviation, kurtosis, and skewness.

[0011] According to another aspect of this application, a particle size performance testing device is provided, comprising: a testing unit for performing particle size testing on a sample to obtain particle size data of particles in the sample; a calculation unit for dividing the particle size data into multiple particle size distribution intervals and calculating a probability density value for each particle size distribution interval, wherein the probability density value is a percentage of a first quantity to a second quantity, the first quantity being the number of particles located within the particle size distribution interval, and the second quantity being the total number of particles in the sample; a generation unit for generating a density distribution feature map based on the probability density value and the particle size distribution interval, wherein the density distribution feature map includes at least one graphic group, a graphic group including multiple sequentially connected graphics, the graphic group corresponding one-to-one with the sample, the maximum width of a graphic corresponding to one of the particle size distribution intervals of the sample, and the graphic attribute of a graphic corresponding to the probability density value of one of the particle size distribution intervals; and a determination unit for determining the particle size performance of the sample based on the density distribution feature map.

[0012] According to another aspect of this application, a computer-readable storage medium is provided, the computer-readable storage medium including a stored program, wherein the program performs any of the methods described.

[0013] According to another aspect of this application, a processor is provided for running a program, wherein the program, when running, performs any of the methods described.

[0014] According to another aspect of this application, a detection system is provided, including a particle size performance detection device, said particle size performance detection device being used to perform any of the methods described.

[0015] Applying the technical solution of this application, in the above-mentioned particle size performance detection method, firstly, the sample is subjected to particle size testing to obtain particle size data of the particles in the sample; then, multiple particle size distribution intervals are divided according to the particle size data, and the probability density value of each particle size distribution interval is calculated. The probability density value is a percentage of a first quantity to a second quantity, where the first quantity is the number of particles located within the particle size distribution interval, and the second quantity is the total number of particles in the sample; subsequently, a density distribution feature map is generated based on the probability density value and the particle size distribution interval. The density distribution feature map includes at least one graphic group, and one graphic group includes multiple sequentially connected graphics. The graphic group corresponds one-to-one with the sample, the maximum width of one graphic corresponds to one of the above-mentioned particle size distribution intervals of the sample, and the graphic attribute of one graphic corresponds to the probability density value of one of the above-mentioned particle size distribution intervals; finally, the particle size performance of the sample is determined based on the density distribution feature map. In the density distribution feature map generated by this detection method, the maximum width of a graphic corresponds to a particle size distribution interval of the sample, and the graphic attribute of the graphic corresponds to the probability density value of the particle size distribution interval. This ensures that each sample in the density distribution feature map uses a corresponding graphic group to represent the relationship between the probability density value and the particle size distribution interval, making the density distribution feature maps of each sample independent and avoiding the superposition of density distribution feature maps between samples. This facilitates the reading of density distribution feature map data, thereby making it easier to determine the particle size performance of the sample based on the read data. Attached Figure Description

[0016] The accompanying drawings, which form part of this application, are used to provide a further understanding of this application. The illustrative embodiments and descriptions of this application are used to explain this application and do not constitute an undue limitation of this application. In the drawings:

[0017] Figure 1 A flowchart of a method for detecting particle size performance according to an embodiment of this application is shown;

[0018] Figure 2 A schematic diagram of a density distribution feature map according to an embodiment of this application is shown;

[0019] Figure 3 A schematic diagram of a particle size performance testing apparatus according to an embodiment of this application is shown. Detailed Implementation

[0020] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0021] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0022] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate for the embodiments of this application described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0023] It should be understood that when an element (such as a layer, film, region, or substrate) is described as being "on" another element, the element may be directly on the other element, or there may be an intermediate element present. Furthermore, in the specification and claims, when an element is described as being "connected" to another element, the element may be "directly connected" to the other element, or "connected" to the other element via a third element.

[0024] As mentioned in the background section, the density distribution feature maps of different samples in the prior art exhibit superposition. To address this issue, in a typical embodiment of this application, a method, apparatus, computer-readable storage medium, processor, and detection system for detecting particle size performance are provided.

[0025] According to an embodiment of this application, a method for detecting particle size performance is provided.

[0026] Figure 1 This is a flowchart of a particle size performance detection method according to an embodiment of this application. Figure 1 As shown, the method includes the following steps:

[0027] Step S101: Perform particle size testing on the sample to obtain particle size data of the particles in the sample.

[0028] Step S102: Divide multiple particle size distribution intervals according to the above particle size data, calculate the probability density value of each of the above particle size distribution intervals, the probability density value is the percentage of the first quantity to the second quantity, the first quantity is the number of particles located in the above particle size distribution interval, and the second quantity is the total number of particles in the above sample.

[0029] Step S103: Generate a density distribution feature map based on the probability density value and the particle size distribution interval. The density distribution feature map includes at least one graphic group. A graphic group includes multiple sequentially connected graphics. The graphic group corresponds one-to-one with the sample. The maximum width of a graphic corresponds to one of the particle size distribution intervals of the sample. The graphic attribute of a graphic represents the probability density value of the particle size distribution interval.

[0030] Step S104: Determine the particle size distribution of the sample based on the density distribution characteristic diagram described above.

[0031] In the above-mentioned particle size performance detection method, firstly, the sample is subjected to particle size testing to obtain particle size data of the particles in the sample; then, multiple particle size distribution intervals are divided according to the particle size data, and the probability density value of each particle size distribution interval is calculated. The probability density value is the percentage of a first quantity to a second quantity, where the first quantity is the number of particles located within the particle size distribution interval, and the second quantity is the total number of particles in the sample; subsequently, a density distribution feature map is generated based on the probability density value and the particle size distribution interval. The density distribution feature map includes at least one graphic group, and each graphic group includes multiple sequentially connected graphics. The graphic group corresponds one-to-one with the sample, the maximum width of a graphic corresponds to one of the particle size distribution intervals of the sample, and the graphic attribute of a graphic corresponds to the probability density value of one of the particle size distribution intervals; finally, the particle size performance of the sample is determined based on the density distribution feature map. In the density distribution feature map generated by this detection method, the maximum width of a graphic corresponds to a particle size distribution interval of the sample, and the graphic attribute of the graphic corresponds to the probability density value of the particle size distribution interval. This ensures that each sample in the density distribution feature map uses a corresponding graphic group to represent the relationship between the probability density value and the particle size distribution interval, making the density distribution feature maps of each sample independent and avoiding the superposition of density distribution feature maps between samples. This facilitates the reading of density distribution feature map data, thereby making it easier to determine the particle size performance of the sample based on the read data.

[0032] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0033] It should also be noted that the particle size data mentioned above refers to particle size R or PHI value. The formula for calculating the PHI value is PHI = log2R. Particle size R can be obtained through particle size testing, and the PHI value is calculated using the above formula. The horizontal axis of the density distribution characteristic map is the particle size R or PHI value, and the vertical axis is the sample number or sample depth. Sample depth refers to the vertical distance from the sample sampling point to the reference surface (usually the wellhead), with the direction downwards from the wellhead being the positive direction. Different samples from the same well represent results from sampling at different depths. Of course, the horizontal axis of the density distribution characteristic map can also be the sample number or sample depth, and the vertical axis can also be the particle size R or PHI value. Furthermore, different sample groups can occupy different positions on the map along the vertical or horizontal axes to avoid overlap, allowing for the creation of density distribution characteristic maps for multiple samples on the same graph. This facilitates the comparison and differentiation of different samples. For example, ... Figure 2 As shown, the horizontal axis of the density distribution feature map represents the PHI value, and the vertical axis represents the sample depth. The graphic groups corresponding to each sample are arranged in parallel along the vertical direction. The probability density value frg is represented by color, and the percentage of the probability density value frg ranges from 0 to 40.

[0034] In one embodiment of this application, the aforementioned graphic attributes include color gradient, dot density, line width, line height, and line density. Specifically, these image attributes are all continuously changing and easy to observe and compare. These image attributes can intuitively display the probability density values ​​of each particle size distribution range, facilitating the comparison and differentiation of different samples. Of course, the aforementioned graphic attributes are not limited to these; those skilled in the art can select other suitable graphic attributes according to the actual situation.

[0035] In one embodiment of this application, the process of dividing the granularity data into multiple granularity distribution intervals and calculating the probability density value of each granularity distribution interval includes: determining a total granularity distribution interval based on the maximum and minimum values ​​of the granularity data; dividing the total granularity distribution interval into multiple preliminary granularity distribution intervals and calculating the probability density value of each preliminary granularity distribution interval; and dividing the preliminary granularity distribution interval into two preliminary granularity distribution intervals when the probability density value of any one of the preliminary granularity distribution intervals is greater than or equal to a predetermined value, until the probability density value of any one of the preliminary granularity distribution intervals is less than the predetermined value, thereby obtaining multiple granularity distribution intervals. Specifically, the predetermined value can be selected according to actual conditions. The method of dividing the granularity distribution intervals ensures that the probability density value of any one of the preliminary granularity distribution intervals is less than the predetermined value, making the interval distribution relatively denser in areas with denser granularity distribution, thus providing a better and more accurate description of the probability density distribution within the high-density granularity distribution intervals.

[0036] In one embodiment of this application, determining the particle size performance of the sample based on the density distribution characteristic map includes: obtaining the particle size parameters of the sample based on the density distribution characteristic map; calculating the particle size distribution parameters based on the particle size parameters; and determining the sorting grade of the sample based on the particle size distribution parameters. Specifically, the relevant particle size parameters are first read from the map, and then the distribution characteristic parameters are calculated. The distribution characteristic parameters characterize the distribution characteristics of the particle size, thereby allowing the particle size to be evaluated based on the distribution characteristic parameters to determine the sorting grade of the sample.

[0037] In one embodiment of this application, the particle size parameters include initial particle size, minimum particle size, average particle size, median particle size, content of different particle sizes, probability density value, and PHI value of each cumulative probability. Specifically, the above particle size parameters can all be directly obtained or calculated from the density distribution feature map. For example, the median particle size Md refers to the particle size corresponding to 50% particle content on the cumulative curve, expressed as particle size or PHI value. The formula for calculating the average particle size Mz is... in, and The particle sizes with percentage contents of 16%, 50%, and 84% on the cumulative curves, respectively.

[0038] In one embodiment of this application, the aforementioned distribution characteristic parameters include sorting coefficient, standard deviation, kurtosis, and skewness. Specifically, the sorting coefficient is the ratio between the 75% and 25% particle sizes on the cumulative particle size curve. Based on the sorting coefficient, sorting performance is divided into three levels: a sorting coefficient greater than or equal to 1 and less than or equal to 2.5 indicates good sorting performance; a sorting coefficient greater than 2.5 and less than or equal to 4.0 indicates moderate sorting performance; and a sorting coefficient greater than 4.0 indicates poor sorting performance. The formula for calculating the standard deviation σ1 is... and The particle sizes representing percentage contents of 5%, 16%, 65%, and 84% on the cumulative curves are used. Based on the sorting coefficient, the sorting performance is divided into seven levels. A standard deviation less than 0.35 indicates excellent sorting performance; a sorting coefficient greater than or equal to 0.35 and less than 0.5 indicates good sorting performance; a sorting coefficient greater than or equal to 0.5 and less than 0.71 indicates relatively good sorting performance; a sorting coefficient greater than or equal to 0.71 and less than 1.00 indicates moderate sorting performance; a sorting coefficient greater than or equal to 1.00 and less than 2.00 indicates poor sorting performance; a sorting coefficient greater than or equal to 2.00 and less than or equal to 4.00 indicates very poor sorting performance; and a standard deviation greater than 4.00 indicates extremely poor sorting performance. The formula for calculating the skewness SK1 is as follows: For the grain size representing 95% of the cumulative curve, when the skewness SK1 equals 0, the percentage content of coarse and fine grain sizes on both sides of the peak decreases accordingly, forming a symmetrical curve with the peak as the axis of symmetry. At this point, the median grain size, average grain size, and mode are all the same value, indicating good sediment sorting. When the skewness SK1 is greater than 0, the curve shape is asymmetrical, with the peak biased towards the coarse-grained side and a low tail on the fine-grained side, indicating that the sediment is predominantly coarse-grained and poorly sorted. When the skewness SK1 is less than 0, the curve shape is also asymmetrical, with the peak biased towards the fine-grained side and a low tail on the coarse-grained side, indicating that the sediment is predominantly fine-grained and poorly sorted.

[0039] This application also provides a particle size performance testing device. It should be noted that the particle size performance testing device of this application can be used to execute the particle size performance testing method provided in this application. The particle size performance testing device provided in this application will be described below.

[0040] Figure 3 This is a schematic diagram of a particle size performance testing device according to an embodiment of this application. Figure 3 As shown, the device includes:

[0041] Test unit 10 is used to perform particle size testing on the sample to obtain particle size data of the particles in the sample.

[0042] The calculation unit 20 is used to divide multiple particle size distribution intervals according to the above particle size data, calculate the probability density value of each of the above particle size distribution intervals, the probability density value is the percentage of a first quantity to a second quantity, the first quantity is the number of particles located in the above particle size distribution interval, and the second quantity is the total number of particles in the above sample.

[0043] The generation unit 30 is used to generate a density distribution feature map based on the probability density value and the particle size distribution interval. The density distribution feature map includes at least one graphic group, and a graphic group includes multiple sequentially connected graphics. The graphic group corresponds one-to-one with the sample. The maximum width of a graphic corresponds to one of the particle size distribution intervals of the sample. The graphic attribute of a graphic corresponds to the probability density value of the particle size distribution interval.

[0044] The determination unit 40 is used to determine the particle size properties of the sample based on the density distribution characteristic map described above.

[0045] In the aforementioned particle size performance testing device, the testing unit performs particle size testing on the sample to obtain particle size data of the particles in the sample; the calculation unit divides the particle size data into multiple particle size distribution intervals and calculates the probability density value of each particle size distribution interval, wherein the probability density value is a percentage of a first quantity to a second quantity, the first quantity being the number of particles located within the particle size distribution interval, and the second quantity being the total number of particles in the sample; the generation unit generates a density distribution feature map based on the probability density value and the particle size distribution interval, wherein the density distribution feature map includes at least one graphic group, and one graphic group includes multiple sequentially connected graphics, the graphic group and the sample are one-to-one, the maximum width of one graphic corresponds to one of the aforementioned particle size distribution intervals of the sample, and the graphic attribute of one graphic corresponds to the probability density value of one of the aforementioned particle size distribution intervals; the determination unit determines the particle size performance of the sample based on the aforementioned density distribution feature map. In the density distribution feature map generated by the detection device, the maximum width of a graphic corresponds to a particle size distribution interval of the sample, and the graphic attribute of the graphic corresponds to the probability density value of the particle size distribution interval. This ensures that each sample in the density distribution feature map uses a corresponding graphic group to represent the relationship between the probability density value and the particle size distribution interval, making the density distribution feature maps of each sample independent and avoiding the superposition of density distribution feature maps between samples. This facilitates the reading of the density distribution feature map data, thereby making it easier to determine the particle size performance of the sample based on the read data.

[0046] It should be noted that the particle size data mentioned above refers to particle size R or PHI value. The formula for calculating the PHI value is PHI = log2R. Particle size R can be obtained through particle size testing, and the PHI value is calculated using the above formula. The horizontal axis of the density distribution characteristic map is the particle size R or PHI value, and the vertical axis is the sample number or sample depth. Sample depth refers to the vertical distance from the sample sampling point to the reference surface (usually the wellhead), with the direction downwards from the wellhead being the positive direction. Different samples from the same well represent results from sampling at different depths. Of course, the horizontal axis of the density distribution characteristic map can also be the sample number or sample depth, and the vertical axis can also be the particle size R or PHI value. Furthermore, different sample groups can occupy different positions on the map along the vertical or horizontal axes to avoid overlap, allowing for the creation of density distribution characteristic maps for multiple samples on the same graph. This facilitates the comparison and differentiation of different samples. For example, ... Figure 2 As shown, the horizontal axis of the density distribution feature map represents the PHI value, and the vertical axis represents the sample depth. The graphic groups corresponding to each sample are arranged in parallel along the vertical direction. The probability density value frg is represented by color, and the percentage of the probability density value frg ranges from 0 to 40.

[0047] In one embodiment of this application, the aforementioned graphic attributes include color gradient, dot density, line width, line height, and line density. Specifically, these image attributes are all continuously changing and easy to observe and compare. These image attributes can intuitively display the probability density values ​​of each particle size distribution range, facilitating the comparison and differentiation of different samples. Of course, the aforementioned graphic attributes are not limited to these; those skilled in the art can select other suitable graphic attributes according to the actual situation.

[0048] In one embodiment of this application, the calculation unit includes a first determining module, a first calculating module, and a processing module. The first determining module determines the total granularity distribution interval based on the maximum and minimum values ​​of the granularity data. The first calculating module divides the total granularity distribution interval into multiple pre-defined granularity distribution intervals and calculates the probability density value of each pre-defined granularity distribution interval. The processing module divides the pre-defined granularity distribution interval into two pre-defined granularity distribution intervals if the probability density value of any pre-defined granularity distribution interval is greater than or equal to a predetermined value, until the probability density value of any pre-defined granularity distribution interval is less than the predetermined value, thus obtaining multiple granularity distribution intervals. Specifically, the predetermined value can be selected according to actual conditions. The method of dividing the granularity distribution intervals ensures that the probability density value of any pre-defined granularity distribution interval is less than the predetermined value, making the interval distribution relatively denser in areas with denser granularity distribution, thereby better and more accurately describing the probability density distribution within the high-density granularity value distribution interval.

[0049] In one embodiment of this application, the determining unit includes an acquisition module, a second calculation module, and a second determining module. The acquisition module acquires the particle size parameters of the sample based on the density distribution feature map. The second calculation module calculates particle size distribution parameters based on the particle size parameters. The second determining module determines the sorting grade of the sample based on the particle size distribution parameters. Specifically, relevant particle size parameters are first read from the map, and then distribution feature parameters are calculated. These distribution feature parameters characterize the distribution characteristics of the particle size distribution, allowing the particle size to be evaluated based on the distribution feature parameters to determine the sorting grade of the sample.

[0050] In one embodiment of this application, the particle size parameters include initial particle size, minimum particle size, average particle size, median particle size, content of different particle sizes, probability density value, and PHI value of each cumulative probability. Specifically, the above particle size parameters can all be directly obtained or calculated from the density distribution feature map. For example, the median particle size Md refers to the particle size corresponding to 50% particle content on the cumulative curve, expressed as particle size or PHI value. The formula for calculating the average particle size Mz is... in, and The particle sizes with percentage contents of 16%, 50%, and 84% on the cumulative curves, respectively.

[0051] In one embodiment of this application, the aforementioned distribution characteristic parameters include sorting coefficient, standard deviation, kurtosis, and skewness. Specifically, the sorting coefficient is the ratio between the 75% and 25% particle sizes on the cumulative particle size curve. Based on the sorting coefficient, sorting performance is divided into three levels: a sorting coefficient greater than or equal to 1 and less than or equal to 2.5 indicates good sorting performance; a sorting coefficient greater than 2.5 and less than or equal to 4.0 indicates moderate sorting performance; and a sorting coefficient greater than 4.0 indicates poor sorting performance. The formula for calculating the standard deviation σ1 is... and The particle sizes representing percentage contents of 5%, 16%, 65%, and 84% on the cumulative curves are used. Based on the sorting coefficient, the sorting performance is divided into seven levels. A standard deviation less than 0.35 indicates excellent sorting performance; a sorting coefficient greater than or equal to 0.35 and less than 0.5 indicates good sorting performance; a sorting coefficient greater than or equal to 0.5 and less than 0.71 indicates relatively good sorting performance; a sorting coefficient greater than or equal to 0.71 and less than 1.00 indicates moderate sorting performance; a sorting coefficient greater than or equal to 1.00 and less than 2.00 indicates poor sorting performance; a sorting coefficient greater than or equal to 2.00 and less than or equal to 4.00 indicates very poor sorting performance; and a standard deviation greater than 4.00 indicates extremely poor sorting performance. The formula for calculating the skewness SK1 is as follows: For the grain size representing 95% of the cumulative curve, when the skewness SK1 equals 0, the percentage content of coarse and fine grain sizes on both sides of the peak decreases accordingly, forming a symmetrical curve with the peak as the axis of symmetry. At this point, the median grain size, average grain size, and mode are all the same value, indicating good sediment sorting. When the skewness SK1 is greater than 0, the curve shape is asymmetrical, with the peak biased towards the coarse-grained side and a low tail on the fine-grained side, indicating that the sediment is predominantly coarse-grained and poorly sorted. When the skewness SK1 is less than 0, the curve shape is also asymmetrical, with the peak biased towards the fine-grained side and a low tail on the coarse-grained side, indicating that the sediment is predominantly fine-grained and poorly sorted.

[0052] This application also provides a detection system, including a particle size performance detection device, which is used to perform any of the above-described methods.

[0053] The aforementioned detection system includes a particle size performance detection device. A testing unit performs particle size testing on a sample to obtain particle size data of the particles in the sample. A calculation unit divides the particle size data into multiple particle size distribution intervals and calculates the probability density value of each particle size distribution interval. The probability density value is a percentage of a first quantity to a second quantity, where the first quantity is the number of particles located within the particle size distribution interval, and the second quantity is the total number of particles in the sample. A generation unit generates a density distribution feature map based on the probability density values ​​and the particle size distribution intervals. The density distribution feature map includes at least one graphic group, and each graphic group includes multiple sequentially connected graphics. Each graphic group corresponds one-to-one with the sample. The maximum width of one graphic corresponds to one of the particle size distribution intervals of the sample, and the graphic attribute of one graphic represents the probability density value of one of the particle size distribution intervals. A determination unit determines the particle size performance of the sample based on the density distribution feature map. In the density distribution feature map generated by the detection device, the maximum width of a graphic corresponds to a particle size distribution interval of the sample, and the graphic attribute of the graphic corresponds to the probability density value of the particle size distribution interval. This ensures that each sample in the density distribution feature map uses a corresponding graphic group to represent the relationship between the probability density value and the particle size distribution interval, making the density distribution feature maps of each sample independent and avoiding the superposition of density distribution feature maps between samples. This facilitates the reading of the density distribution feature map data, thereby making it easier to determine the particle size performance of the sample based on the read data.

[0054] The aforementioned particle size performance testing device includes a processor and a memory. The aforementioned testing unit, calculation unit, generation unit, and determination unit are all stored in the memory as program units, and the processor executes the aforementioned program units stored in the memory to realize the corresponding functions.

[0055] The processor contains a kernel, which retrieves the corresponding program units from memory. One or more kernels can be configured, and adjusting kernel parameters can address the problem of overlapping density distribution feature maps of different samples in existing technologies.

[0056] The memory may include non-permanent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0057] This invention provides a computer-readable storage medium having a program stored thereon, which, when executed by a processor, implements the above-described method.

[0058] This invention provides a processor for running a program, wherein the program executes the method described above when it runs.

[0059] This invention provides a device including a processor, a memory, and a program stored in the memory and executable on the processor. When the processor executes the program, it performs at least the following steps:

[0060] Step S101: Perform particle size testing on the sample to obtain particle size data of the particles in the sample.

[0061] Step S102: Divide multiple particle size distribution intervals according to the above particle size data, calculate the probability density value of each of the above particle size distribution intervals, the probability density value is the percentage of the first quantity to the second quantity, the first quantity is the number of particles located in the above particle size distribution interval, and the second quantity is the total number of particles in the above sample.

[0062] Step S103: Generate a density distribution feature map based on the probability density value and the particle size distribution interval. The density distribution feature map includes at least one graphic group. A graphic group includes multiple sequentially connected graphics. The graphic group corresponds one-to-one with the sample. The maximum width of a graphic corresponds to one of the particle size distribution intervals of the sample. The graphic attribute of a graphic represents the probability density value of the particle size distribution interval.

[0063] Step S104: Determine the particle size distribution of the sample based on the density distribution characteristic diagram described above.

[0064] The devices mentioned in this article can be servers, PCs, tablets, mobile phones, etc.

[0065] This application also provides a computer program product, which, when executed on a data processing device, is suitable for executing an initialization program having at least the following method steps:

[0066] Step S101: Perform particle size testing on the sample to obtain particle size data of the particles in the sample.

[0067] Step S102: Divide multiple particle size distribution intervals according to the above particle size data, calculate the probability density value of each of the above particle size distribution intervals, the probability density value is the percentage of the first quantity to the second quantity, the first quantity is the number of particles located in the above particle size distribution interval, and the second quantity is the total number of particles in the above sample.

[0068] Step S103: Generate a density distribution feature map based on the probability density value and the particle size distribution interval. The density distribution feature map includes at least one graphic group. A graphic group includes multiple sequentially connected graphics. The graphic group corresponds one-to-one with the sample. The maximum width of a graphic corresponds to one of the particle size distribution intervals of the sample. The graphic attribute of a graphic represents the probability density value of the particle size distribution interval.

[0069] Step S104: Determine the particle size distribution of the sample based on the density distribution characteristic diagram described above.

[0070] In the above embodiments of the present invention, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0071] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units described above can be a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0072] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0073] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0074] If the aforementioned integrated units are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a computer-readable storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned computer-readable storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0075] As can be seen from the above description, the embodiments of this application achieve the following technical effects:

[0076] 1) In the particle size performance testing method of this application, firstly, the sample is subjected to particle size testing to obtain particle size data of the particles in the sample; then, multiple particle size distribution intervals are divided according to the particle size data, and the probability density value of each particle size distribution interval is calculated. The probability density value is a percentage of a first quantity to a second quantity, where the first quantity is the number of particles located within the particle size distribution interval, and the second quantity is the total number of particles in the sample; subsequently, a density distribution feature map is generated based on the probability density value and the particle size distribution interval. The density distribution feature map includes at least one graphic group, and one graphic group includes multiple sequentially connected graphics. The graphic group corresponds one-to-one with the sample. The maximum width of one graphic corresponds to one of the particle size distribution intervals of the sample, and the graphic attribute of one graphic corresponds to the probability density value of one of the particle size distribution intervals; finally, the particle size performance of the sample is determined based on the density distribution feature map. In the density distribution feature map generated by this detection method, the maximum width of a graphic corresponds to a particle size distribution interval of the sample, and the graphic attribute of the graphic corresponds to the probability density value of the particle size distribution interval. This ensures that each sample in the density distribution feature map uses a corresponding graphic group to represent the relationship between the probability density value and the particle size distribution interval, making the density distribution feature maps of each sample independent and avoiding the superposition of density distribution feature maps between samples. This facilitates the reading of density distribution feature map data, thereby making it easier to determine the particle size performance of the sample based on the read data.

[0077] 2) In the particle size performance testing device of this application, the testing unit performs particle size testing on the sample to obtain particle size data of the particles in the sample; the calculation unit divides multiple particle size distribution intervals according to the particle size data and calculates the probability density value of each particle size distribution interval, wherein the probability density value is a percentage of a first quantity to a second quantity, the first quantity is the number of particles located within the particle size distribution interval, and the second quantity is the total number of particles in the sample; the generation unit generates a density distribution feature map according to the probability density value and the particle size distribution interval, wherein the density distribution feature map includes at least one graphic group, a graphic group includes multiple sequentially connected graphics, the graphic group corresponds one-to-one with the sample, the maximum width of a graphic corresponds to one of the particle size distribution intervals of the sample, and the graphic attribute of a graphic corresponds to the probability density value of one of the particle size distribution intervals; the determination unit determines the particle size performance of the sample according to the density distribution feature map. In the density distribution feature map generated by the detection device, the maximum width of a graphic corresponds to a particle size distribution interval of the sample, and the graphic attribute of the graphic corresponds to the probability density value of the particle size distribution interval. This ensures that each sample in the density distribution feature map uses a corresponding graphic group to represent the relationship between the probability density value and the particle size distribution interval, making the density distribution feature maps of each sample independent and avoiding the superposition of density distribution feature maps between samples. This facilitates the reading of the density distribution feature map data, thereby making it easier to determine the particle size performance of the sample based on the read data.

[0078] 3) The detection system of this application includes a particle size performance detection device. The testing unit performs particle size testing on the sample to obtain particle size data of the particles in the sample. The calculation unit divides the particle size data into multiple particle size distribution intervals and calculates the probability density value of each particle size distribution interval. The probability density value is a percentage of a first quantity to a second quantity. The first quantity is the number of particles located within the particle size distribution interval, and the second quantity is the total number of particles in the sample. The generation unit generates a density distribution feature map based on the probability density value and the particle size distribution interval. The density distribution feature map includes at least one graphic group. One graphic group includes multiple sequentially connected graphics. The graphic group corresponds one-to-one with the sample. The maximum width of one graphic corresponds to one of the particle size distribution intervals of the sample. The graphic attribute of one graphic corresponds to the probability density value of one of the particle size distribution intervals. The determination unit determines the particle size performance of the sample based on the density distribution feature map. In the density distribution feature map generated by the detection device, the maximum width of a graphic corresponds to a particle size distribution interval of the sample, and the graphic attribute of the graphic corresponds to the probability density value of the particle size distribution interval. This ensures that each sample in the density distribution feature map uses a corresponding graphic group to represent the relationship between the probability density value and the particle size distribution interval, making the density distribution feature maps of each sample independent and avoiding the superposition of density distribution feature maps between samples. This facilitates the reading of the density distribution feature map data, thereby making it easier to determine the particle size performance of the sample based on the read data.

[0079] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A method of detecting particle size performance, characterized by, The method comprises the following steps: performing particle size test on a sample to obtain particle size data of particles in the sample; dividing a plurality of particle size distribution intervals according to the particle size data, and calculating a probability density value of each particle size distribution interval, the probability density value being a percentage of a first quantity to a second quantity, the first quantity being a number of particles located in the particle size distribution interval, and the second quantity being a total number of particles in the sample; generating a density distribution feature map according to the probability density value and the particle size distribution interval, the density distribution feature map comprising at least one graph group, one graph group comprising a plurality of sequentially connected graphs, the graph group and the sample corresponding to each other, a maximum width of one graph corresponding to one particle size distribution interval of the sample, and a graph attribute of one graph corresponding to the probability density value of one particle size distribution interval, the graph attribute comprising a color gradient, a point density, a line width, a line height, and a line density; determining a particle size performance of the sample according to the density distribution feature map; determining a particle size performance of the sample according to the density distribution feature map, comprising: obtaining a particle size parameter of the sample according to the density distribution feature map; calculating a particle size distribution parameter according to the particle size parameter; determining a sorting grade of the sample according to the particle size distribution parameter, the particle size parameter comprising an initial particle size, a minimum particle size, an average particle size, a median particle size, a content of different particle sizes, a probability density value, and a PHI value of each cumulative probability, and the particle size distribution parameter comprising a sorting coefficient, a standard deviation, a kurtosis, and a skewness.

2. The method of claim 1, wherein, dividing a plurality of particle size distribution intervals according to the particle size data, and calculating a probability density value of each particle size distribution interval, comprising: determining a total particle size distribution interval according to a maximum value and a minimum value of the particle size data; equally dividing the total particle size distribution interval into a plurality of preliminary particle size distribution intervals, and calculating a probability density value of each preliminary particle size distribution interval; in a case where the probability density value of the preliminary particle size distribution interval is greater than or equal to a predetermined value, equally dividing the preliminary particle size distribution interval into two preliminary particle size distribution intervals, until the probability density value of any one of the preliminary particle size distribution intervals is less than the predetermined value, to obtain a plurality of particle size distribution intervals.

3. A particle size performance detection device, characterized by, The method comprises the following steps: a test unit configured to perform particle size test on a sample to obtain particle size data of particles in the sample; a calculation unit configured to divide a plurality of particle size distribution intervals according to the particle size data, and calculate a probability density value of each particle size distribution interval, the probability density value being a percentage of a first quantity to a second quantity, the first quantity being a number of particles located in the particle size distribution interval, and the second quantity being a total number of particles in the sample; The generating unit is configured to generate a density distribution feature map according to the probability density value and the particle size distribution interval, the density distribution feature map comprising at least one graph group, one graph group comprising a plurality of sequentially connected graphs, the graph group and the sample corresponding one by one, the maximum width of one graph corresponding to one particle size distribution interval of the sample, the graph attribute of one graph corresponding to the probability density value of one particle size distribution interval, the graph attribute comprising a color gradient, a point density, a line width, a line height, and a line density. The determining unit is configured to determine the particle size performance of the sample according to the density distribution feature map. The determining unit comprises an obtaining module, a second calculating module, and a second determining module, wherein the obtaining module is configured to obtain the particle size parameter of the sample according to the density distribution feature map; the second calculating module is configured to calculate a particle size distribution parameter according to the particle size parameter; and the second determining module is configured to determine the sorting grade of the sample according to the particle size distribution parameter, the particle size parameter comprising an initial particle size, a minimum particle size, an average particle size, a median particle size, a different particle size content, a probability density value, and a PHI value of each cumulative probability, and the particle size distribution parameter comprising a sorting coefficient, a standard deviation, a kurtosis, and a skewness.

4. A computer-readable storage medium, characterized in that, The computer readable storage medium comprises a stored program, wherein the program executes the method of claim 1 or 2.

5. A processor, comprising: The processor is configured to run a program, wherein the program executes the method of claim 1 or 2 when running.

6. A detection system comprising a detection device of particle properties, characterized in that The particle size performance detection device is configured to execute the method of claim 1 or 2.

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