Method for determining particle size distribution of aggregate, system and electronic device

Abstract

SOLUTION: A method for determining a particle size distribution of aggregates and a system therefor, and electronic equipment are disclosed, with the present disclosure relating to a road surface engineering technology field. The method includes: extracting a sample from a measurement object aggregate and constructing a set of aggregate particles, determining a likelihood function of the measurement object aggregate by using machine vision detection technology, and further determining a plurality of posterior probabilities by using a first Bayesian formula, then constructing a set of binomial distributions in accordance with particle quantities of different actual particle diameter intervals; determining a set of posterior distributions by using a second Bayesian formula, assuming a beta distribution as prior distribution, on the basis of the particle quantities of different actual particle size intervals and the set of binomial distributions, and determining the particle size distribution of the measurement object aggregate in accordance with the set of posterior distributions.EFFECT: The present invention determines a posterior probability by constructing a likelihood function and a prior probability and further determines a set of posterior distributions, enabling quickly determining the particle size distribution of aggregate in a coarse aggregate and further improve the efficiency of determining asphalt performance.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to the field of road surface construction, and in particular to a method for determining the particle size distribution of aggregates, and a system and electronic device therefor. [Background technology]

[0002] Asphalt mixtures are multiphase composites composed of asphalt adhesive, aggregate particles, and fillers. Coarse aggregate accounts for over 90% of the total mass, and the performance of asphalt mixtures is fundamentally dependent on the properties of the coarse aggregate. Research has shown that the first major factor affecting asphalt mixture performance is whether aggregate particle size distribution conforms to specifications. Road construction standards require aggregate particle size distribution to be achieved through sieving. However, aggregate frequently clogs the sieve during sieving, necessitating manual clearance. This results in low detection efficiency and requires manual data analysis of the sieved aggregate, which cannot meet the needs of mixing station staff for rapid sieving of aggregate particle size. In actual construction, the absolute error of aggregate particle size distribution at each interval must not exceed ±5%. Summary of the Invention [Problem to be solved by the invention]

[0003] The present invention aims to provide a method for determining the particle size distribution of aggregate, a system for the method, and an electronic device that can quickly determine the particle size distribution of aggregate in coarse aggregate and further improve the efficiency of determining the performance of asphalt. [Means for solving the problem]

[0004] In order to achieve the above object, the present invention provides the following solutions. The method for determining the particle size distribution of aggregate of the present invention comprises: Extracting a sample from the aggregate to be measured to construct a set of aggregate particles including a plurality of aggregate particle subsets, wherein the actual particle sizes of all aggregate particles in the same aggregate particle subset all belong to the same actual particle size interval, the aggregate particle subsets correspond one-to-one to the actual particle size intervals, and the intersection of any two of the actual particle size intervals is an empty set; determining a likelihood function for the aggregate particles based on the aggregate particle set using machine vision detection technology, and the likelihood function is used to describe the probability distribution relationship of the aggregate particles in different actual particle size intervals and different ferret particle size intervals; Obtaining a prior probability of the aggregate to be measured, the prior probability being determined by the mass of a single aggregate particle in different actual particle size intervals; determining a plurality of posterior probabilities using a first Bayes formula according to the likelihood function and the prior probability; and i |x i ) is the actual particle size interval c of each aggregate in the i-th ferret particle size interval xi i Denote the probability at converting the prior probabilities of all aggregate particles in the subset of aggregate particles from sums of mass ratios to particle quantity ratios of the corresponding actual particle size intervals; constructing a set of binomial distributions according to particle quantity ratios of different actual particle size intervals, and the binomial distributions in the set of binomial distributions correspond one-to-one to the actual particle size intervals; Based on the number of particles at different actual particle size intervals and a set of binomial distributions, a set of posterior distributions is determined using the second Bayes formula with a beta distribution as a prior distribution, and the posterior distributions in the set of posterior distributions correspond one-to-one to the actual particle size intervals; and determining the particle size distribution of the aggregate to be measured according to the set of posterior distributions.

[0005] Optionally, determining a likelihood function for the measured aggregate based on the aggregate particle population using machine vision detection techniques includes: determining the ferret particle size of each of all particles in the aggregate collection utilizing machine vision detection techniques; constructing a plurality of ferret particle size intervals, and the intersection of any two of the ferret particle size intervals is an empty set; and determining a plurality of likelihood function values ​​P(x i |c i ) is the i-th aggregate subset c i The ferret particle size of the aggregate in the i-th ferret particle size interval x i indicates the probability of belonging to

[0006] Optionally, determining a particle size distribution of the aggregate to be measured in response to the set of posterior distributions includes: determining one posterior distribution in the set of posterior distributions as a current posterior distribution; and determining the expected value of the current posterior distribution as the quantity proportion of the actual particle size interval to which the measured aggregate corresponds.

[0007] Optionally, the first Bayes formula is TIFF0007811030000001.tif24170, where N is the number of actual particle size intervals, and P(c i ) is the i-th aggregate subset c i is the prior probability of

[0008] The system for determining the particle size distribution of aggregates is an aggregate particle set construction module for extracting a sample from the aggregate to be measured and constructing a set of aggregate particles, the set of aggregate particles including a plurality of aggregate particle subsets, the actual particle sizes of all aggregate particles in the same aggregate particle subset all belong to the same actual particle size interval, the aggregate particle subsets correspond one-to-one to the actual particle size intervals, and the intersection sets of any two of the actual particle size intervals are all empty sets; a likelihood function determination module for determining a likelihood function of the measured aggregate based on the aggregate particle set using machine vision detection technology, the likelihood function being used to describe the probability distribution relationship of aggregate particles in different actual particle size intervals and different ferret particle size intervals; a prior probability determination module for obtaining a prior probability of the aggregate to be measured, the prior probability being determined by the mass of a single aggregate particle in different actual particle size intervals; a posterior probability determination module for determining a plurality of posterior probabilities using a first Bayes formula according to the likelihood function and the prior probability; and i |x i ) is the particle size interval x of the ith ferret i The actual particle size interval c of each aggregate in i Denote the probability at an actual particle size interval particle quantity determination module for converting the prior probabilities of all aggregate particles in the subset of aggregate particles from a sum of mass ratios to a particle quantity ratio of a corresponding actual particle size interval; a binomial distribution set construction module for constructing a binomial distribution set according to particle quantity ratios of different actual particle size intervals, wherein the binomial distributions in the binomial distribution set correspond one-to-one to the actual particle size intervals; a posterior distribution set determination module for determining a posterior distribution set using the second Bayes formula with a beta distribution as a prior distribution based on the particle counts of different actual particle size intervals and a set of binomial distributions, wherein the posterior distributions in the set of posterior distributions correspond one-to-one to the actual particle size intervals; The particle size distribution of the aggregate to be measured is determined according to the set of posterior distributions.

[0009] An electronic device comprising a memory and a processor, the memory being used to store a computer program, and the processor executing the computer program, so that the electronic device can perform the above-mentioned method for determining particle size distribution of aggregates of the present invention.

[0010] Optionally, said memory is a readable storage medium. [Effects of the Invention]

[0011] According to the specific embodiments provided by the present invention, the present invention discloses the following technical effects.

[0012] The present invention provides a method, system, and electronic device for determining the particle size distribution of aggregate, which involves extracting a sample from a target aggregate to construct a set of aggregate particles, using machine vision detection technology to determine a likelihood function for the target aggregate based on the set of aggregate particles, obtaining a prior probability for the target aggregate, using a first Bayes formula to determine multiple posterior probabilities based on the likelihood function and the prior probabilities, determining that the sum of the prior probabilities of all aggregate particles in the subset of aggregate particles is the particle quantity for the corresponding actual particle size interval, constructing a set of binomial distributions based on the particle quantities for the different actual particle size intervals, and using a second Bayes formula to determine a set of posterior distributions based on the particle quantities for the different actual particle size intervals and the set of binomial distributions, using a beta distribution as the prior distribution, and determining the particle size distribution of the target aggregate based on the set of posterior distributions. By constructing the likelihood function and prior probabilities to determine the posterior probabilities and then determining the set of posterior distributions, the present invention can quickly determine the particle size distribution of aggregate in coarse aggregate and further improve the efficiency of asphalt performance determination. [Brief explanation of the drawings]

[0013] In order to more clearly describe the embodiments of the present invention or the technical solutions in the prior art, the drawings necessary to be used in the embodiments will be briefly described below. Obviously, the drawings in the following description are only some embodiments of the present invention, and those skilled in the art can further obtain other drawings based on these drawings without any creative efforts.

[0014] [Figure 1] 1 is a flowchart showing a method for determining particle size distribution of aggregates in Example 1 of the present invention. [Figure 2] FIG. 2 is a schematic diagram illustrating collection of aggregate images in the first embodiment of the present invention. [Figure 3] FIG. 1 is a diagram showing the actual particle size intervals between different Feret particle sizes in Example 1 of the present invention. [Figure 4] 1 is a flowchart showing steps for estimating particle size distribution of aggregate in Example 1 of the present invention. [Figure 5] FIG. 10 is a first posterior distribution diagram for estimating that aggregates of S9 specification are in the actual particle size interval c1 in Example 1 of the present invention. [Figure 6] FIG. 10 is a second posterior distribution diagram for estimating that aggregates of S9 specification are in the actual particle size interval c1 in Example 1 of the present invention. [Figure 7] FIG. 10 is a third posterior distribution diagram for estimating that aggregates with S9 specifications are in the actual particle size interval c1 in Example 1 of the present invention. [Figure 8] FIG. 4 is a fourth posterior distribution diagram for estimating that aggregates with S9 specifications are in the actual particle size interval c1 in Example 1 of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0015] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only some embodiments of the present invention, not all embodiments, and all other embodiments obtained by those skilled in the art based on the embodiments of the present invention without any creative efforts are all within the protection scope of the present invention.

[0016] The present invention aims to provide a method for determining the particle size distribution of aggregate, a system therefor, and an electronic device that can quickly determine the particle size distribution of aggregate in coarse aggregate and further improve the efficiency of determining the performance of asphalt.

[0017] In order to make the above objects, features and advantages of the present invention more apparent and comprehensible, the present invention will be described in more detail below with reference to the drawings and detailed description of the invention.

[0018] Example 1 The present invention uses the maximum Feret particle size of aggregate particles measured by machine vision as collected data, and there is an error between it and the actual particle size of aggregate particles. However, the actual particle sizes of aggregates in different Feret particle size intervals still overlap (as shown in Figure 3). The present invention reduces the error caused by this using Bayes' formula, and can correctly estimate the proportion of aggregates with different particle sizes (i.e., particle size distribution). To facilitate understanding of aggregate particle size, i (0 <i<15,i∈N * ) is used to represent the actual particle size interval of the aggregate, and this interval range is determined by JTG F40-2004 《Technical Specification for Road Asphalt Surface Construction》, and x i (0 <i<15,i∈N * ) represents the Feret particle size interval of the aggregate.

[0019] As shown in FIG. 1 and FIG. 4, this embodiment provides a method for determining particle size distribution of aggregate, which includes the following steps: Step 101: Extract a sample from the aggregate to be measured to construct a set of aggregate particles, the set of aggregate particles includes multiple aggregate particle subsets, the actual particle sizes of all aggregate particles in the same aggregate particle subset all belong to the same actual particle size interval, the aggregate particle subsets correspond one-to-one to the actual particle size intervals, and the common set of any two actual particle size intervals is an empty set. Step 102: Based on the aggregate particle set, a likelihood function of the measured aggregate is determined using machine vision detection technology, and the likelihood function is used to describe the probability distribution relationship of aggregate particles in different actual particle size intervals and different ferret particle size intervals. Step 102 includes: Step 1021: Using machine vision detection technology, the ferret particle size of every particle in each aggregate set was determined. Step 1022: A plurality of ferret particle size intervals are constructed, and the intersection of any two ferret particle size intervals is the empty set. Step 1023: Determine a plurality of likelihood function values ​​according to a plurality of ferret particle sizes, and calculate the likelihood function values ​​P(x i |ci ) is the i-th aggregate subset c i The ferret particle size of the aggregate in the i-th ferret particle size interval x i The probability of belonging to

[0020] Regarding how to obtain the likelihood function in Bayes' formula, first, let us consider the different c i 1000 pieces of each experimental aggregate are obtained, and then a set D is obtained using machine vision measurement. i where x indicates all experimental aggregates belonging to i The number of particles in the interval is n i It was.

[0021] where: TIFF0007811030000002.tif16170. This gives the likelihood function Listed TIFF0007811030000003.tif16170.

[0022] Taking ln from the above equation, we obtain the following equation. TIFF0007811030000004.tif16170

[0023] where: Since TIFF0007811030000005.tif20170 is a known condition, the Lagrange multiplier method is used for the above equation. TIFF0007811030000006.tif18170 was used.

[0024] Find the partial derivative with respect to θ, set the partial derivative function equal to 0, I got TIFF0007811030000007.tif17170.

[0025] As a result, TIFF0007811030000008.tif23170 can be derived and obtained.

[0026] therefore I get TIFF0007811030000009.tif15170.

[0027] The solution is the degree function value θ i It is possible to obtain aggregates with c i If the Feret particle size measured by machine vision belongs to x i The probability of belonging to is P(x i |c i ) was shown to be

[0028] Step 103: Obtain a prior probability for the aggregate to be measured. The prior probability is determined by the mass of a single aggregate particle at different actual particle size intervals. Because the mixing station staff uses a sieve mesh screening method to determine whether the aggregate specifications comply with the standard (JTG F40-2004 "Technical Specifications for Road Asphalt Surface Construction"), the present invention uses the aggregate specifications in JTG F40-2004 as the prior probability. In actual construction, the sieve mesh screening method is used to estimate the aggregate particle size distribution, which is determined by the mass of aggregate passing through each level of sieve mesh as a percentage of the total mass. In the present invention, the aggregate particle size distribution is estimated by the quantity of aggregate at different particle size intervals as a percentage of the total quantity. Therefore, to facilitate the calculation of the present invention, it is necessary to estimate the relationship between aggregate mass and quantity.

[0029] Varies depending on the screening method using sieve meshc i The particle count of the experimental aggregate is 1000 pieces each, and then the same c i Weigh the aggregates and calculate the corresponding different c i The bone mass of the bone mass is obtained, and the different ci in accordance with the JTG F40-2004 standard with the quantity as the unit are calculated using the prior probability P(c i ) was obtained.

[0030] Step 104: Determine a plurality of posterior probabilities using the first Bayes formula according to the likelihood function and the prior probability, and calculate the posterior probability P(c i |x i ) is the particle size interval x of the ith ferreti The probability of each aggregate in the i-th actual particle size interval ci is shown.

[0031] Here, the first Bayes formula is TIFF0007811030000010.tif26170, where N is the number of actual particle size intervals, and P(c i ) is the i-th aggregate subset c i was the prior probability.

[0032] Step 105: The prior probabilities of all aggregate particles in the subset of aggregate particles were converted from the sum of mass ratios to particle quantity ratios of the corresponding actual particle size intervals. Step 106: A set of binomial distributions is constructed according to the particle quantity ratios of different actual particle size intervals, and the binomial distributions in this set of binomial distributions correspond one-to-one to the above-mentioned actual particle size intervals. The quality inspector at the mixing station obtained the test sample (the number of particles was about 3000, and the quantity was recorded as M) by sampling. After the test sample was visually measured, the Feret particle size data set was obtained under visual measurement. Then, the Bayes formula was applied to calculate the test sample, and the posterior probability P(c i |x i ) was obtained. P(c i |x i ) are different c i Similarly, the probability p value of each individual aggregate in i It can be understood as the distribution value of a single stone in each c i By accumulating the p values ​​of the measured samples in the interval, the measured sample c with different aggregate specifications i At this time, we obtain the number of possible particles for each c i Quantity m i The probability in this interval is considered to be a binomial distribution B(M,θ), which gives the set of binomial distributions {B1(M,θ1),B2(M,θ2),B3(M,θ3)…,B 15 (M,θ 15 )} was obtained.

[0033] Step 107: Based on the particle quantity of different actual particle size intervals and the set of binomial distributions, the beta distribution is used as the prior distribution, and a set of posterior distributions is determined using the second Bayes formula, and the posterior distributions in the set of posterior distributions correspond one-to-one to the previous actual particle size intervals. Step 108: The particle size distribution of the aggregate to be measured is determined according to the set of posterior distributions.

[0034] Step 108 is Step 1081 of determining one of the posterior distributions in the set of previous posterior distributions as the current posterior distribution; and step 1082 of determining the expected value of the current posterior distribution as the quantity proportion in the actual particle size interval corresponding to the measured aggregate. In actual construction, the particle size distribution of aggregate is estimated according to the proportion of bone material content in different intervals, and the relationship ratio between bone material content and quantity is obtained through experiments, and by calculation, the mass ratio of aggregate in different intervals can be obtained, and the accuracy of the present invention is evaluated by comparing with the results obtained by a manual sieving method using a sieve mesh.

[0035] Estimating the particle size distribution of aggregates is the i The purpose is to estimate the quantity proportion of the measured sample aggregate in the i According to step 3, each c i Obtain a set of binomial distributions of the measured samples in the interval and calculate the beta distribution f(θ i ) as the prior distribution, and apply Bayesian to obtain the posterior distribution f(θ i |m i ) was estimated, where f(θ i |m i )∝f(θ i )p(m i |θ i ), the probability density function of the prior distribution is TIFF0007811030000011.tif24170. The probability density function of the binomial distribution is The file is TIFF0007811030000012.tif24170.

[0036] therefore, The formula obtained was TIFF0007811030000013.tif18170.

[0037] Since the prior distribution has already been determined, the parameters α and β are known, as well as the total number of samples M and c i The quantity m belonging to i is known, so TIFF0007811030000014.tif17170 and TIFF0007811030000015.tif19170 is the parameter to be estimated θ i It was confirmed that there is no relation between the above equations, and the above equations were combined and normalized to obtain the following equation. TIFF0007811030000016.tif17170

[0038] As can be seen from the above equation, the obtained posterior distribution is a beta distribution, and its parameter is α'=α+m i , β'=β+Mm i where the expectation of the beta distribution is expressed as α' / (α'+β'), and the expected value obtained is the parameter to be estimated, θ i is an estimate of all c i By estimating the quantity proportion of the measured samples in the aggregate, it was possible to estimate the particle size distribution of the aggregate based on Bayesian statistics.

[0039] In the present invention, as shown in Figure 2, the maximum Feret particle size of the aggregate was obtained by uniformly dropping experimental aggregate using a vibrating material feeder, collecting images of individual aggregates, and then calibrating and converting the pixel size of the aggregate images. In the present invention, the maximum Feret particle size of aggregate particles obtained by machine vision measurement was used as the collected data, but there was an error between this and the actual particle size of the aggregate particles. As shown in Figure 3, aggregates with different Feret particle size intervals still overlap, so there was an error in the equivalent actual particle size interval between the maximum Feret particle size interval. This error was reduced by using Bayes' formula. Here, the aggregate particle size obtained by sieving using a sieve mesh is authoritative, and in the present invention, the aggregate particle size obtained by this method was used as the actual particle size.

[0040] As shown in Figure 4, the present invention takes the S9 specification coarse aggregate in the standard as an example, and the actual particle size intervals of the aggregate of this specification are c1 (19-26.5) mm, c2 (16-19) mm, c3 (13.2-16) mm, c4 (9.5-13.2) mm, and c5 (4.75-9.5) mm.

[0041] (1) To facilitate the estimation of the actual particle size intervals of the aggregates, the aggregates were uniformly sieved in 1 mm intervals, and 1,000 experimental aggregates were obtained for each interval. By solving the likelihood function, it was determined that the aggregates were c i If the Feret particle size measured by machine vision belongs to (i∈{1,2,3,4,5}), then i The probability of belonging to (i∈{1,2,3,4,5}) was obtained and shown in Table 1.

[0042] [Table 1]

[0043] (2) Prior probability P(c i In order to obtain the c value, the present inventors estimated the relationship between bone mass and quantity through experiments. iThe particle count of the experimental aggregate is 1000 pieces each, and then the same c i Weigh the aggregates and calculate the corresponding different c i The present invention repeats the above steps three times, selects the average value from the obtained results, and then calculates the aggregate quantity ratio that complies with the aggregate specifications in JTG F40-2004, as shown in Table 2.

[0044] [Table 2]

[0045] (3) The quality inspector at the mixing station obtained the test sample (the number of particles was approximately 3,000, and the quantity was recorded as M) through sample sampling. After the test sample was visually measured, a set of Feret particle size data was obtained under visual measurement. Then, Bayes' formula was applied to calculate the test sample. According to Bayes' formula, the posterior probability can be determined by dividing the product of the prior probability and the likelihood by the sum of the product of the prior probability and the likelihood. TIFF0007811030000019.tif30170

[0046] where N is the number of actual particle size intervals of the aggregate, where N=5.

[0047] [Table 3]

[0048] As shown in Table 3, applying Bayes' formula, the posterior probability P(c i |x i ) and obtain different c i Similarly, the probability values ​​of each individual aggregate in i It can be understood as the distribution value of a single stone in each c i By accumulating the probability values ​​of the measured samples at intervals, the c iThe possible particle numbers of the measured sample were obtained. When M = 9, 90, 900, and 3000, the estimated and calculated c i The possible numbers of aggregate particles in the sample were obtained and are shown in Table 4.

[0049] [Table 4]

[0050] (4) Estimation is performed to obtain the posterior distribution, and the expected value obtained is the estimated quantity proportion of aggregate particle size c1. As shown in Figures 5 to 8, the abscissa indicates the likelihood estimation, and the ordinate indicates the posterior distribution. As the aggregate particle quantity M increases, the obtained posterior distribution becomes more concentrated, and the certainty of the estimated parameter θ1 increases. At the same time, the estimated quantity proportion of aggregates belonging to c1 gradually approaches the actual quantity proportion. Here, the parameters α and β of the prior beta distribution are set to 1, and the possible particle quantity of the measured sample belonging to the c1 interval is calculated and obtained as 29.926. The formula obtained in Step 4 Substitute TIFF0007811030000022.tif28170, Similarly, the other intervals were calculated and obtained as shown in Table 5. The mass fraction of each interval was calculated using the relationship ratio between the bone material content and quantity obtained in the experiment, thereby realizing estimation of the aggregate particle size distribution. Here, the absolute error was basically kept within ±5%.

[0051] [Table 5]

[0052] From the above analysis, it can be seen that when the number of aggregates is small, the influence from prior distribution to posterior distribution is greatest. However, as the number of aggregates increases, the influence from prior distribution to posterior distribution gradually decreases. Therefore, it can be reasonably assumed that after the number of aggregates increases to a certain level, the influence from prior distribution to posterior distribution becomes negligible. The present invention uses Bayesian statistics to estimate the particle size distribution of aggregates, effectively improving screening efficiency. Compared to traditional manual screening of aggregates, this method, combined with machine vision measurement technology, achieves rapid screening, while eliminating the influence of human factors throughout the entire detection process, effectively ensuring the quality of aggregate screening.

[0053] Based on the rapid development of machine vision and the improved accuracy of image collection information, the present invention uses Bayesian statistical estimation to estimate the particle size distribution of aggregates, which has a higher level of automation than conventional methods and can quickly detect whether the conveyed aggregates comply with the JTG F40-2004 standard at the mixing station. Furthermore, when the number of aggregates is sufficiently large, the present invention can estimate the particle size distribution of unacceptable aggregates, making it applicable to aggregates of all specifications. The particle size distribution of aggregates estimated by this method is highly accurate, providing a new method and perspective for future aggregate particle size screening.

[0054] Example 2 To achieve the corresponding functions and technical effects, a system for determining particle size distribution of aggregates is provided below to implement the method corresponding to Example 1 above. The system for determining the particle size distribution of aggregate in Example 2 includes an aggregate particle set construction module for extracting a sample from the aggregate to be measured and constructing an aggregate particle set including a plurality of aggregate particle subsets; the actual particle sizes of all aggregate particles in the same aggregate particle subset all belong to the same actual particle size interval, and the aggregate particle subset corresponds one-to-one with the actual particle size interval, and the common set of any two actual particle size intervals is an empty set; a likelihood function determination module for determining a likelihood function of the measured aggregate based on the aggregate particle set using machine vision detection technology, the likelihood function being used to describe the probability distribution relationship of the aggregate particles in different actual particle size intervals and different ferret particle size intervals; a prior probability determination module for obtaining a prior probability of the aggregate to be measured, the prior probability being determined by the mass of a single aggregate particle in different actual particle size intervals; a posterior probability determination module for determining a plurality of posterior probabilities using the first Bayes formula according to the likelihood function and the prior probability; and a posterior probability determination module for determining a plurality of posterior probabilities P(c i |x i ) is the particle size interval x of the ith ferret i The i-th actual particle size interval c i indicates the probability of each aggregate in an actual particle size interval particle count determination module for determining that the sum of prior probabilities of all aggregate particles in the subset of aggregate particles is the particle count for the corresponding actual particle size interval; a binomial distribution set construction module for constructing a binomial distribution set according to the particle quantity of different actual particle size intervals, and the binomial distribution in the binomial distribution set corresponds one-to-one with the actual particle size intervals; and a posterior distribution set determination module for determining a posterior distribution set using the second Bayes formula with a beta distribution as a prior distribution based on the particle counts of different actual particle size intervals and a set of binomial distributions, wherein the posterior distributions in the posterior distribution set correspond one-to-one to the actual particle size intervals.

[0055] The particle size distribution of the aggregate to be measured was determined according to the set of posterior distributions.

[0056] Example 3 This example provides an electronic device, which includes a memory and a processor, wherein the memory is used to store a computer program, and the processor executes the computer program, causing the electronic device to perform the method for determining particle size distribution of aggregate described in Example 1.

[0057] Here, the memory is a readable storage medium.

[0058] Each embodiment in this specification is described step by step, and the main points described in each embodiment are the differences from other embodiments, and the same or similar parts between the embodiments can be referred to each other. The systems disclosed in the embodiments correspond to the methods disclosed in the embodiments, so the explanation is relatively simple, and for related information, refer to the explanation of the method part.

[0059] In this specification, the principles and embodiments of the present invention are explained by applying specific examples, and the explanation of the above examples is only to contribute to understanding the method of the present invention and its core idea, and at the same time, those skilled in the art can change the form for implementing the invention and the application scope according to the idea of ​​the present invention. Therefore, the contents of this specification should not be understood as limiting the present invention.

Claims

1. 1. A method for determining particle size distribution of aggregate, comprising: Extracting a sample from the aggregate to be measured to construct a set of aggregate particles including a plurality of aggregate particle subsets, wherein the actual particle sizes of all aggregate particles in the same aggregate particle subset all belong to the same actual particle size range, the aggregate particle subsets correspond one-to-one to the actual particle size ranges, and the intersection of any two of the actual particle size ranges is an empty set; Based on the aggregate particle set, using machine vision detection technology, determine the Feret particle size of all particles in the aggregate particle set, respectively, and construct a plurality of Feret particle size ranges; the intersection of any two of the Feret particle size ranges is an empty set, and determine a likelihood function of the measured aggregate according to the plurality of Feret particle sizes; and calculate the likelihood function value P(x ｉ |c ｉ ) is the i-th aggregate subset c ｉ The Feret particle size of the aggregate in the i-th Feret particle size range x ｉ the likelihood function is used to describe the probability distribution relationship of aggregate particles in each actual particle size range and each corresponding Feret particle size range; Obtaining a prior probability of the aggregate to be measured, the prior probability being determined by the mass of a single aggregate particle in each actual particle size range, and the prior probability is the aggregate specification in JTG F40-2004; determining a plurality of posterior probabilities using a first Bayes formula according to the likelihood function and the prior probability; and ｉ |x ｉ ) is the ith Feret particle size range x ｉ The i-th actual particle size range c of each aggregate in ｉ Denote the probability at The first Bayes formula is: and where N is the number of particles in the actual particle size range, and P(c ｉ ) is the i-th aggregate subset c ｉ is the prior probability of The prior probability of all aggregate particles in the aggregate particle subset is calculated from the sum of the mass ratios by the particle quantity ratio θ of the corresponding actual particle size range. i and converting it to constructing a set of binomial distributions according to the particle quantity ratio of each actual particle size range, and the binomial distributions in the set of binomial distributions correspond one-to-one to the actual particle size ranges; The probability density function of the binomial distribution is where M represents the number of particles in the aggregate particle set, and m ｉ is the i-th aggregate subset c ｉ indicates the number of particles belonging to determining a set of posterior distributions using the second Bayes formula with a beta distribution as a prior distribution based on the particle counts of each actual particle size range and a set of binomial distributions, and the posterior distributions in the set of posterior distributions correspond one-to-one to the actual particle size ranges; The probability density function of the beta distribution is and The second Bayes formula is: where the parameters α and β are both 1; The particle size distribution of the aggregate to be measured is determined according to the set of the posterior distributions, and the particle size distribution of the aggregate to be measured is determined within the i-th actual particle size range c ｉ A method for determining the particle size distribution of aggregate, characterized in that the particle size distribution is the quantity ratio of the measured sample aggregate in the sample.

2. Determining the particle size distribution of the aggregate to be measured according to the set of posterior distributions includes: determining one posterior distribution in the set of posterior distributions as a current posterior distribution; A method for determining the particle size distribution of aggregates as described in claim 1, characterized in that it includes determining the expected value of the current posterior distribution as the quantity proportion in the actual particle size range corresponding to the measured aggregates.

3. 1. A system for determining particle size distribution of aggregates, comprising: an aggregate particle set construction module for extracting a sample from the aggregate to be measured and constructing a set of aggregate particles, the set of aggregate particles including a plurality of aggregate particle subsets, wherein the actual particle sizes of all aggregate particles in the same aggregate particle subset all belong to the same actual particle size range, the aggregate particle subsets correspond one-to-one to the actual particle size ranges, and the intersection of any two of the actual particle size ranges is an empty set; According to the aggregate particle set, using machine vision detection technology to determine the Feret particle size of all particles in the aggregate particle set, respectively, to construct a plurality of Feret particle size ranges; the intersection of any two of the Feret particle size ranges is an empty set; a likelihood function determination module for determining a likelihood function of the measured aggregate according to a plurality of Feret particle sizes; and a likelihood function value P(x ｉ |c ｉ ) is the i-th aggregate subset c ｉ The Feret particle size of the aggregate in the i-th Feret particle size range x ｉ the likelihood function is used to describe the probability distribution relationship of aggregate particles in each actual particle size range and each corresponding Feret particle size range; a prior probability determination module for obtaining a prior probability of the aggregate to be measured, the prior probability being determined by the mass of a single aggregate particle in each actual particle size range, and the prior probability being the aggregate specification in JTG F40-2004; a posterior probability determination module for determining a plurality of posterior probabilities using a first Bayes formula according to the likelihood function and the prior probability; ｉ |x ｉ ) is the ith Feret particle size range x ｉ The i-th actual particle size range c of each aggregate in ｉ Denote the probability at The first Bayes formula is: and where N is the number of particles in the actual particle size range, and P(c ｉ ) is the i-th aggregate subset c ｉ is the prior probability of The prior probability of all aggregate particles in the aggregate particle subset is calculated from the sum of the mass ratios by the particle quantity ratio θ of the corresponding actual particle size range. i and a particle quantity determination module for determining the actual particle size range to convert it into a binomial distribution set construction module for constructing a binomial distribution set according to the particle quantity ratio of each actual particle size range, wherein the binomial distributions in the binomial distribution set correspond one-to-one to the actual particle size ranges; The probability density function of the binomial distribution is where M represents the number of particles in the aggregate particle set, and m ｉ is the i-th aggregate subset c ｉ indicates the number of particles belonging to a posterior distribution set determination module for determining a posterior distribution set based on the particle count of each actual particle size range and a set of binomial distributions using a beta distribution as a prior distribution and the second Bayes formula, wherein the posterior distributions in the set of posterior distributions correspond one-to-one to the actual particle size ranges; The probability density function of the beta distribution is and The second Bayes formula is: where the parameters α and β are both 1; The particle size distribution of the aggregate to be measured is determined according to the set of the posterior distributions, and the particle size distribution of the aggregate to be measured is determined within the i-th actual particle size range c ｉ A system for determining the particle size distribution of aggregates, characterized in that the particle size distribution is the quantity ratio of the measured sample aggregates in the system.

4. An electronic device comprising a memory and a processor, the memory being used to store a computer program, and the processor executing the computer program so that the electronic device can perform the method for determining the particle size distribution of aggregates according to claim 1 or claim 2.

5. 5. The electronic device according to claim 4, wherein the memory is a readable storage medium.

Images