Silica sol production quality assessment system and method based on image analysis

Abstract

The present application discloses a silica sol production quality assessment system and method based on image analysis, which belongs to the field of silica sol production quality assessment. The present application obtains silica sol image data, pre-processes the obtained image, constructs a silica sol structure evolution model, imports silica sol particle parameters into the silica sol structure evolution model to calculate silica sol particle structure characteristic parameters, evaluates the aging evolution degree of the silica sol, constructs a light stratification attenuation model, imports the incident light intensity into the light stratification attenuation model to calculate the total light dose of each layer, constructs an aging evolution model, and imports the total light dose into the aging evolution model to evaluate the future aging of the silica sol. The present application extracts characterization parameters such as particle size, roundness, and aggregation through image analysis, and combines the modeling of the light dose received by each layer of the silica sol to improve the visual monitoring accuracy of the silica sol aging process and the aging trend prediction ability.

Description

Technical Field

[0001] The present application relates to the field of silica sol production quality assessment, and more specifically to a silica sol production quality assessment system and method based on image analysis. Background Art

[0002] Silica sol, as an important colloidal dispersion system, is widely used in ceramics, casting, coatings, electronic packaging, and other fields. Its performance is affected by factors such as particle stability and degree of agglomeration, and it gradually deteriorates over time, leading to performance degradation. Existing technologies primarily use chemical analysis, particle size distribution measurements, or accelerated storage experiments to assess its aging behavior. However, these methods are time-consuming and costly, lack systematic modeling of light-related factors, are unable to simulate the cumulative damage to material properties caused by light aging, and lack the ability to predict future conditions.

[0003] This application uses image analysis to extract particle size, roundness, aggregation and other characterization parameters, combines it with modeling of the light dose received by each layer of silica sol, evaluates the degree of aging in real time, and sets an aging failure threshold to reversely infer the shelf life of the silica sol. It overcomes the limitations of traditional methods such as the inability to dynamically monitor, difficulty in simulating the photoaging process, and lack of predictive ability, and improves the visual monitoring accuracy of the silica sol aging process and the ability to predict aging trends. Summary of the Invention

[0004] In response to the shortcomings of the existing technology, this application proposes a silica sol production quality assessment system and method based on image analysis.

[0005] To achieve the above objectives, this application provides the following technical solutions:

[0006] The silica sol production quality assessment system and method based on image analysis includes the following specific steps:

[0007] Acquire silica sol image data and pre-process the acquired image;

[0008] Construct a silica sol structure evolution model, import silica sol particle parameters into the silica sol structure evolution model to calculate silica sol particle structure characteristic parameters, and evaluate the aging evolution degree of silica sol;

[0009] Construct a light layer attenuation model and import the incident light intensity into the light layer attenuation model to calculate the total light dose of each layer;

[0010] An aging evolution model was constructed, and the total light dose was introduced into the aging evolution model to evaluate the future aging of silica sol.

[0011] Preferably, the step of acquiring silica sol image data and preprocessing the acquired image comprises the following specific steps:

[0012] S11. After the silica sol sample is produced, microscopic images are collected at different storage time points to obtain ambient lighting data. The image collection equipment includes but is not limited to an optical microscope, a scanning electron microscope, and an automatic imaging system equipped with a macro camera. Image collection is performed under standard light conditions and a standard background plate.

[0013] S12. Perform standardized preprocessing on the collected images to remove background interference, enhance particle boundary features, and improve subsequent recognition accuracy. Convert the images into binary form to distinguish particle areas from background areas. Extract each particle area from the binary image. Identify all interconnected pixel areas as independent particle objects through connected domain labeling. Use contour extraction algorithm to identify the boundary pixels of each area.

[0014] Preferably, the step of constructing a silica sol structure evolution model, importing silica sol particle parameters into the silica sol structure evolution model to calculate silica sol particle structure characteristic parameters, and evaluating the aging evolution degree of the silica sol comprises the following specific steps:

[0015] S21. Substituting the particle pixel area in the image into the silica sol particle size calculation formula to calculate the silica sol particle size, wherein the silica sol particle size calculation formula for the jth silica sol particle is: ,in, is the actual pixel area of ​​the jth silica sol particle, and the particle size of the silica sol particle is substituted into the average particle size calculation formula to calculate the average particle size value, where the average particle size calculation formula is: , where m is the total number of particles. The particle size and average particle size of the silica sol particles are substituted into the particle size distribution standard deviation calculation formula to evaluate the discreteness of the particles in the silica sol. The particle size distribution standard deviation calculation formula is: ;

[0016] S22. Substitute the pixel area and perimeter of the silica sol particles into the particle average roundness calculation formula to evaluate the shape regularity of the particles in the silica sol, wherein the particle average roundness calculation formula is: ,in, is the circumference of the jth particle, m is the total number of particles, if the particles are close to the ideal state, that is, circular, then Approaching 1, if the particles agglomerate or stick together, the boundary is irregular, reduce;

[0017] S23. Substituting the number of agglomerated and connected silica sol particles into the aggregation calculation formula to evaluate the adhesion between particles, where the aggregation is the ratio of the number of agglomerated and connected particles to the total number of particles. If the minimum distance between the boundaries of two particles is less than a set threshold, it is considered to be adhesion or agglomeration;

[0018] S24. Subtract the particle size distribution standard deviation, average particle roundness, and aggregation degree at the current moment from those at the initial moment, respectively. Substitute the obtained difference in particle size distribution standard deviation, average particle roundness, and aggregation degree between the current moment and the initial moment into the aging degree calculation formula to evaluate the aging degree of the silica sol. The aging degree calculation formula is: ,in, 、 and They are particle size distribution weight, particle roundness weight and aggregation weight respectively. The optimal combination can be fitted through the historical aging data of different batches of samples. is the standard deviation of the particle size distribution, is the average roundness difference of the particles, is the aggregation difference.

[0019] Preferably, the step of constructing a light layer attenuation model and importing the incident light intensity into the light layer attenuation model to calculate the total light dose of each layer comprises the following specific steps:

[0020] S31, the silica sol is evenly divided into N layers from top to bottom, wherein the thickness of each silica sol layer is: , H is the total thickness of the silica sol, and the refractive index of the solvent and the refractive index of silica are substituted into the refractive index calculation formula of the silica sol to calculate the refractive index of each layer of the silica sol, where the refractive index of the i-th layer of silica sol is: ,in, The refractive index of the solvent used to prepare the silica sol is is the refractive index of solid silicon dioxide, is the volume fraction of the particles in the i-th layer, where the volume fraction of the particles in the i-th layer is calculated as follows: ,in, is the total area of ​​particle pixels in the image, is the total area of ​​the image, is the empirical concentration attenuation coefficient, which determines the concentration change rate. According to Fresnel's law, the refractive index of each layer of silica sol is substituted into the interface reflectivity calculation formula to calculate the interface reflectivity of each layer. The interface reflectivity calculation formula from the i-1th layer to the ith layer is: ;

[0021] S32. Substitute the interface reflectivity and the incident light intensity into the light intensity calculation formula to calculate the light intensity of each layer. The light intensity calculation formula for the i-th layer is: ,in, is the incident light intensity, is the reflection loss caused by the refractive index jump between the r-1th layer and the rth layer, r represents the cumulative multiplication from the 1st layer to the i-th layer, is the light absorption coefficient of silica sol, which indicates the degree of absorption of light intensity per unit length. is the transmittance from the r−1th layer to the rth layer, It is used to represent the continuous attenuation process, the attenuation process of light when it propagates in an absorbing medium. In the medium, light is absorbed by molecules and the intensity decreases exponentially. After passing through the absorbing medium with a thickness of Δh, the light intensity decays to its original value. times;

[0022] S33. Substitute the light intensity into the total light dose calculation formula to calculate the total light dose received by the i-th layer of silica sol within time t. The total light dose calculation formula received by the i-th layer of silica sol within time t is: ,in, For time The actual light intensity received by the i-th layer at the moment, is the integration time variable, As unit time.

[0023] Preferably, the construction of the aging evolution model and the introduction of the total light dose into the aging evolution model to evaluate the future aging of the silica sol include the following specific steps:

[0024] S41. Substituting the total light dose into the photoaging efficiency calculation formula to calculate the photoaging index, wherein the photoaging efficiency calculation formula is: , the light aging efficiency is substituted into the future aging degree calculation formula to evaluate the future aging of silica sol, where the future aging degree calculation formula is: ,in, For the future time period, is the predicted total amount of light in the future time period, is the aging rate factor;

[0025] S42. According to the use requirements of silica sol, an aging failure threshold is set. When the aging degree reaches the aging failure threshold, the silica sol is aged to an unacceptable performance state. The remaining validity period of the silica sol is calculated based on the aging failure threshold and the future aging degree calculation formula. When the aging degree approaches the aging failure threshold, the silica sol is marked as abnormal.

[0026] The silica sol production quality assessment system based on image analysis is implemented based on the silica sol production quality assessment method based on image analysis described above, and specifically includes:

[0027] A data acquisition module is used to acquire silica sol image data and pre-process the acquired image;

[0028] Silica sol structure evolution module, used to calculate silica sol particle structure characteristic parameters through silica sol particle parameters and evaluate the aging evolution degree of silica sol;

[0029] Light layer attenuation module, used to calculate the total light dose of each layer through the incident light intensity and quantify the energy input of photoaging;

[0030] Aging evolution module for evaluating future silica sol aging using total light dose.

[0031] An electronic device comprises: a processor and a memory, wherein the memory stores a computer program that can be called by the processor;

[0032] The processor executes the above-mentioned silica sol production quality assessment method based on image analysis by calling the computer program stored in the memory.

[0033] A computer-readable storage medium is characterized in that it stores instructions, which, when executed on a computer, enable the computer to execute the above-mentioned silica sol production quality assessment method based on image analysis.

[0034] Compared with the prior art, the present invention has the following advantages:

[0035] This application obtains silica sol image data, pre-processes the acquired images, constructs a silica sol structure evolution model, imports silica sol particle parameters into the silica sol structure evolution model to calculate silica sol particle structure characteristic parameters, evaluates the aging evolution degree of silica sol, constructs a light stratification attenuation model, imports the incident light intensity into the light stratification attenuation model to calculate the total light dose of each layer, constructs an aging evolution model, and imports the total light dose into the aging evolution model to evaluate the future aging of silica sol. This application extracts particle size, roundness, aggregation and other characterization parameters through image analysis, and combines the light dose received by each layer of silica sol to model the improvement of the visual monitoring accuracy of the silica sol aging process and the aging trend prediction capability. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 This is a schematic diagram of the overall process of the silica sol production quality assessment method based on image analysis in this application;

[0037] Figure 2 This is the flow chart for calculating the light aging efficiency for this application;

[0038] Figure 3 This is a schematic diagram of the overall framework of the silica sol production quality assessment system based on image analysis in this application. DETAILED DESCRIPTION

[0039] The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments.

[0040] Example 1

[0041] See also Figure 1 and Figure 2 , this application provides an embodiment: a method for evaluating the production quality of silica sol based on image analysis, which includes the following specific steps:

[0042] Acquire silica sol image data and pre-process the acquired image;

[0043] Construct a silica sol structure evolution model, import silica sol particle parameters into the silica sol structure evolution model to calculate silica sol particle structure characteristic parameters, and evaluate the aging evolution degree of silica sol;

[0044] Construct a light layer attenuation model, import the incident light intensity into the light layer attenuation model to calculate the total light dose of each layer, which is used to quantify the energy input basis of photoaging;

[0045] An aging evolution model was constructed, and the total light dose was introduced into the aging evolution model to evaluate the future aging of silica sol.

[0046] In this embodiment, it should be specifically explained that obtaining silica sol image data and preprocessing the obtained image include the following specific steps:

[0047] S11. After the silica sol sample is produced, microscopic images are collected at different storage time points to obtain ambient lighting data. The image collection equipment includes but is not limited to an optical microscope, a scanning electron microscope, and an automatic imaging system equipped with a macro camera. Image collection is performed under standard light conditions and a standard background plate to ensure consistency and comparability of the image data.

[0048] S12. Perform standardized preprocessing on the collected images to remove background interference, enhance particle boundary features, and improve subsequent recognition accuracy. Use the adaptive threshold method to convert the image into binary form to distinguish between particle areas and background areas. Extract each particle area from the binary image. Use the connected domain labeling to identify all interconnected pixel areas as independent particle objects. Use the contour extraction algorithm to identify the boundary pixels of each area to reduce the impact of background interference on subsequent analysis results.

[0049] In this embodiment, it should be specifically explained that constructing a silica sol structure evolution model, importing silica sol particle parameters into the silica sol structure evolution model to calculate silica sol particle structure characteristic parameters, and evaluating the aging evolution degree of silica sol include the following specific steps:

[0050] S21. Substituting the particle pixel area in the image into the silica sol particle size calculation formula to calculate the silica sol particle size, wherein the silica sol particle size calculation formula for the jth silica sol particle is: ,in, is the actual pixel area of ​​the jth silica sol particle. The actual pixel area reflects the size of the particle. When aging or agglomeration occurs, the average particle size will increase significantly. Substitute the silica sol particle size into the average particle size calculation formula to calculate the average particle size value. The average particle size calculation formula is: , average particle size, reflects the overall particle size level, and is used to monitor particle expansion or agglomeration. Where m is the total number of particles. Substitute the silica sol particle size and average particle size into the particle size distribution standard deviation calculation formula to evaluate the discreteness of the particles in the silica sol. The particle size distribution standard deviation calculation formula is: The standard deviation is used to quantify the degree of dispersion of the particle size distribution, indicating the trend of uniformity and stability of the silica sol particle system. A small standard deviation indicates that the particle size distribution is concentrated and the system structure is relatively uniform. A large standard deviation indicates that the particle size distribution is dispersed, and there may be particle agglomeration and abnormal growth.

[0051] S22. Substitute the pixel area and perimeter of the silica sol particles into the particle average roundness calculation formula to evaluate the shape regularity of the particles in the silica sol, wherein the particle average roundness calculation formula is: ,in, is the circumference of the jth particle, m is the total number of particles, if the particles are close to the ideal state, that is, circular, then Approaching 1, if the particles agglomerate or stick together, the boundary is irregular, The average roundness is calculated by the particle area and circumference to evaluate the regularity of the particle boundary. The lower the roundness, the higher the aging or agglomeration phenomenon.

[0052] S23. Substitute the number of agglomerated and connected silica sol particles into the aggregation calculation formula to evaluate the adhesion between particles, where the aggregation is the ratio of the number of agglomerated and connected particles to the total number of particles. If the minimum distance between the boundaries of two particles is less than a set threshold, it is considered adhesion and agglomeration. The method for identifying agglomeration is to determine that the minimum distance between the boundaries of two particles is less than a set threshold (e.g., 3 pixels). This method intuitively quantifies the degree of adhesion and agglomeration between particles in the silica sol system, reflecting the local dense accumulation of particles caused by factors such as aging, sedimentation, and surface energy changes;

[0053] S24. Subtract the particle size distribution standard deviation, average particle roundness, and aggregation degree at the current moment from those at the initial moment, respectively. Substitute the obtained difference in particle size distribution standard deviation, average particle roundness, and aggregation degree between the current moment and the initial moment into the aging degree calculation formula to evaluate the aging degree of the silica sol. The aging degree calculation formula is: ,in, 、 and They are the particle size distribution weight, particle roundness weight and aggregation weight respectively, and the optimal combination is fitted through the historical aging data of different batches of samples. is the standard deviation of the particle size distribution, is the average roundness difference of the particles, The aggregation difference is calculated by taking the difference between the standard deviation of particle size distribution, average roundness of particles and aggregation between the current moment and the initial moment, and combining the weighted summation to comprehensively reflect the evolution of silica sol particles in size, shape and agglomeration state.

[0054] In this embodiment, it should be specifically explained that constructing a light layer attenuation model and importing the incident light intensity into the light layer attenuation model to calculate the total light dose of each layer includes the following specific steps:

[0055] S31, the silica sol is evenly divided into N layers from top to bottom, wherein the thickness of each silica sol layer is: , H is the total thickness of the silica sol, and the refractive index of the solvent and the refractive index of silica are substituted into the refractive index calculation formula of the silica sol to calculate the refractive index of each layer of the silica sol, where the refractive index of the i-th layer of silica sol is: ,in, The refractive index of the solvent used to prepare the silica sol is is the refractive index of solid silicon dioxide, is the volume fraction of the particles in the i-th layer. The silica sol is composed of a solid phase (silicon dioxide particles) and a liquid phase (solvent). Each small layer is regarded as a uniformly mixed system, and the refractive index is the weighted average of the volume fractions of the two. The volume fraction of the particles in the i-th layer is calculated as follows: ,in, is the total area of ​​particle pixels in the image, is the total area of ​​the image, is the empirical concentration attenuation coefficient, which determines the rate of concentration change. In silica sol, particles slowly sink under the action of gravity during static storage, with low concentration in the upper layer and high concentration in the lower layer, and the change is nonlinear and intensified. i is the layer number from top to bottom, and the concentration decays exponentially with the number of layers. According to Fresnel's law, the refractive index of each layer of silica sol is substituted into the interface reflectivity calculation formula to calculate the interface reflectivity of each layer. The interface reflectivity calculation formula from the i-1th layer to the ith layer is: Before light enters the i-th layer, some reflection loss will occur at the interface, causing the light intensity to gradually attenuate as it penetrates deeper layers. The silica sol is layered from top to bottom and the refractive index and interface reflectivity of each layer are calculated to simulate the propagation and attenuation process of light inside the silica sol. This reflects the impact of the non-uniform concentration distribution caused by particle sedimentation on the optical properties. The refractive index is calculated by volume fraction weighting. The layered calculation of the silica sol not only improves the spatial resolution of the light intensity distribution, but also provides a more accurate input basis for the subsequent photoaging dose calculation.

[0056] For example, the refractive index of each layer can also be measured layer by layer using an ellipsometer, an Abbe refractometer, or an interferometer;

[0057] S32. Substitute the interface reflectivity and the incident light intensity into the light intensity calculation formula to calculate the light intensity of each layer. The light intensity calculation formula for the i-th layer is: ,in, is the incident light intensity, which indicates the light intensity entering the top layer (0th layer) of silica sol from air or light source. It is the initial value of the entire light propagation process. is the reflection loss caused by the refractive index jump between the r-1th and rth layers, r represents the cumulative multiplication from the 1st layer to the i-th layer, is the light absorption coefficient of silica sol, which indicates the degree of absorption of light intensity per unit length and is obtained by fitting the experimental spectrum transmittance curve. is the transmittance from the r−1th layer to the rth layer, It is used to represent the continuous attenuation process, the attenuation process of light when it propagates in an absorbing medium. In the medium, light is absorbed by molecules and the intensity decreases exponentially. After passing through the absorbing medium with a thickness of Δh, the light intensity decays to its original value. The reflection loss caused by the interface refractive index jump is reflected by the cumulative multiplication of the interlayer reflectivity, reflecting the energy loss of light at the interface of different media. The absorption process of light inside each layer of the medium is described by the exponential decay model. Combined with the absorption coefficient fitted by the experiment, the precise quantification of the decrease of light intensity with thickness is achieved;

[0058] S33. Substitute the light intensity into the total light dose calculation formula to calculate the total light dose received by the i-th layer of silica sol within time t. The total light dose calculation formula received by the i-th layer of silica sol within time t is: ,in, For time The actual light intensity received by the i-th layer at the moment, is the integration time variable, The cumulative light dose is obtained by integral calculation per unit time. It not only takes into account the spatial attenuation characteristics of light intensity with depth, but also reflects the actual light conditions of the silica sol exposed to light for a long time through the integral processing of the light intensity changing with time, avoiding the one-sided influence of instantaneous light intensity on aging assessment. The cumulative light doses at different levels reflect the spatial non-uniformity of the aging degree inside the silica sol.

[0059] In this embodiment, it should be specifically explained that the construction of the aging evolution model and the introduction of the total light dose into the aging evolution model to evaluate the future aging of the silica sol include the following specific steps:

[0060] S41. Substituting the total light dose into the photoaging efficiency calculation formula to calculate the photoaging index, wherein the photoaging efficiency calculation formula is: , the light aging efficiency is substituted into the future aging degree calculation formula to evaluate the future aging of silica sol, where the future aging degree calculation formula is: ,in, For the future time period, is the predicted total amount of light in the future time period, It is the aging rate factor used to control the growth rate. It can be automatically updated by curve fitting through the initial short-term aging experiment. The aging process is slow start, accelerated deterioration, and stable approaching the limit, which is consistent with exponential growth. As time goes by, the exponential term approaches 1, indicating that aging is saturated. By establishing a functional relationship between light exposure and aging, combined with the cumulative light dose and photoaging efficiency, the nonlinear evolution law of silica sol material from initial stability, gradual degradation, to final aging saturation is reflected.

[0061] S42. According to the use requirements of silica sol, an aging failure threshold is set. When the aging degree reaches the aging failure threshold, the silica sol is aged to an unacceptable performance state. The remaining validity period of the silica sol is calculated based on the aging failure threshold and the future aging degree calculation formula. When the aging degree approaches the aging failure threshold, the silica sol is marked as abnormal.

[0062] The advantages of this embodiment over the prior art are:

[0063] This application obtains silica sol image data, pre-processes the acquired images, constructs a silica sol structure evolution model, imports silica sol particle parameters into the silica sol structure evolution model to calculate silica sol particle structure characteristic parameters, evaluates the aging evolution degree of silica sol, constructs a light stratification attenuation model, imports the incident light intensity into the light stratification attenuation model to calculate the total light dose of each layer, constructs an aging evolution model, and imports the total light dose into the aging evolution model to evaluate the future aging of silica sol. This application extracts particle size, roundness, aggregation and other characterization parameters through image analysis, and combines the light dose received by each layer of silica sol to model the improvement of the visual monitoring accuracy of the silica sol aging process and the aging trend prediction capability.

[0064] Example 2

[0065] like Figure 3As shown, a silica sol production quality assessment system based on image analysis is implemented based on the above-mentioned silica sol production quality assessment method based on image analysis, and specifically includes a data acquisition module, a silica sol structure evolution module, a light stratification attenuation module and an aging evolution module. The data acquisition module is used to acquire silica sol image data and pre-process the acquired image; the silica sol structure evolution module is used to calculate the silica sol particle structure characteristic parameters through the silica sol particle parameters and evaluate the aging evolution degree of the silica sol; the light stratification attenuation module is used to calculate the total light dose of each layer through the incident light intensity and quantify the energy input of light aging; the aging evolution module is used to evaluate the future aging of the silica sol through the total light dose.

[0066] Example 3

[0067] This embodiment provides an electronic device, comprising: a processor and a memory, wherein the memory stores a computer program that can be called by the processor;

[0068] The processor executes the above-mentioned silica sol production quality assessment method based on image analysis by calling the computer program stored in the memory.

[0069] This electronic device can vary significantly depending on its configuration or performance. It can include one or more processors (Central Processing Units, CPUs) and one or more memories, wherein the memories store at least one computer program, which is loaded and executed by the processor to implement the method for assessing silica sol production quality based on image analysis provided in the above-mentioned method embodiment. The electronic device can also include other components for implementing the device's functions. For example, the electronic device can also include components such as wired or wireless network interfaces and input / output interfaces for data input and output. This embodiment is not described in detail here.

[0070] Example 4

[0071] This embodiment provides a computer-readable storage medium having a rewritable computer program stored thereon;

[0072] When the computer program is run on a computer device, the computer device is caused to execute the above-mentioned method for evaluating the production quality of silica sol based on image analysis.

[0073] For example, computer-readable storage media can be read-only memory (ROM), random access memory (RAM), compact disc read-only memory (CD-ROM), magnetic tape, floppy disk, and optical data storage device.

[0074] The above embodiments can be implemented in whole or in part via software, hardware, firmware, or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. A computer program product comprises one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the processes or functions according to the embodiments of the present application are fully or partially generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. Computer instructions can be stored in a computer-readable storage medium or transferred from one computer-readable storage medium to another. For example, computer instructions can be transferred from one website, computer, server, or data center to another website, computer, server, or data center via a wired network or / and a wireless network. A computer-readable storage medium can be any available medium accessible by a computer or a data storage device such as a server or data center that contains a collection of one or more available media. Available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media. Semiconductor media can be solid-state drives.

Claims

1. A method for evaluating the production quality of silica sol based on image analysis, characterized in that: It includes the following specific steps: Acquire silica sol image data and pre-process the acquired image; A silica sol structure evolution model is constructed, silica sol particle parameters are imported into the silica sol structure evolution model to calculate silica sol particle structural characteristic parameters, and the aging evolution degree of silica sol is evaluated, including the following specific steps: the particle pixel area in the image is substituted into the silica sol particle size calculation formula to calculate the silica sol particle size, where the calculation formula for the j-th silica sol particle size is: ,in, is the actual pixel area of ​​the jth silica sol particle. The particle size of the silica sol particle is substituted into the average particle size calculation formula to calculate the average particle size value. The particle size and average particle size of the silica sol particle are substituted into the particle size distribution standard deviation calculation formula to evaluate the discreteness of the particles in the silica sol. The pixel area and perimeter of the silica sol particle are substituted into the particle average roundness calculation formula to evaluate the shape regularity of the particles in the silica sol. The particle average roundness calculation formula is: ,in, is the circumference of the jth particle, m is the total number of particles, the number of agglomerated and connected silica sol particles is substituted into the aggregation calculation formula to evaluate the adhesion between particles, where the aggregation is the ratio of the number of agglomerated and connected particles to the total number of particles, the particle size distribution standard deviation, particle average roundness and aggregation at the current moment are subtracted from the initial moment, and the obtained difference in particle size distribution standard deviation, particle average roundness and aggregation at the current moment and the initial moment are substituted into the aging calculation formula to evaluate the aging degree of silica sol, where the aging calculation formula is: ,in, 、 and are the particle size distribution weight, particle roundness weight and aggregation weight, respectively. is the standard deviation of the particle size distribution, is the average roundness difference of the particles, is the aggregation difference; Construct a light layer attenuation model and import the incident light intensity into the light layer attenuation model to calculate the total light dose of each layer; An aging evolution model was constructed, and the total light dose was introduced into the aging evolution model to evaluate the future aging of silica sol.

2. The method for evaluating the production quality of silica sol based on image analysis according to claim 1, wherein: The construction of the light layer attenuation model and the importation of the incident light intensity into the light layer attenuation model to calculate the total light dose of each layer include the following specific steps: The silica sol is evenly divided into N layers from top to bottom. The refractive index of the solvent and the refractive index of silica are substituted into the refractive index calculation formula of the silica sol to calculate the refractive index of each layer of the silica sol. The refractive index of the i-th layer of silica sol is: ,in, The refractive index of the solvent used to prepare the silica sol is is the refractive index of solid silicon dioxide, is the volume fraction of the particles in the i-th layer, where the volume fraction of the particles in the i-th layer is calculated as follows: ,in, is the total area of ​​particle pixels in the image, is the total area of ​​the image, is the empirical concentration attenuation coefficient. According to Fresnel's law, the refractive index of each layer of silica sol is substituted into the interface reflectivity calculation formula to calculate the interface reflectivity of each layer. The interface reflectivity calculation formula from the i-1th layer to the ith layer is: ; Substitute the interface reflectivity and incident light intensity into the light intensity calculation formula to calculate the light intensity of each layer. The light intensity calculation formula for the i-th layer is: ,in, is the incident light intensity, is the thickness of each silica sol layer, is the reflection loss caused by the refractive index jump between the r-1th and rth layers, r is the cumulative loss from the 1st layer to the i-th layer, is the light absorption coefficient of silica sol; Substitute the light intensity into the total light dose calculation formula to calculate the total light dose received by the i-th layer of silica sol within time t. The total light dose calculation formula received by the i-th layer of silica sol within time t is: ,in, For time The actual light intensity received by the i-th layer at the moment, is the integration time variable, As unit time.

3. The method for evaluating the production quality of silica sol based on image analysis according to claim 2, wherein: The construction of the aging evolution model and the introduction of the total light dose into the aging evolution model to evaluate the future aging of the silica sol include the following specific steps: Substitute the total light dose into the photoaging efficiency calculation formula to calculate the photoaging index, where the photoaging efficiency calculation formula is: , the light aging efficiency is substituted into the future aging degree calculation formula to evaluate the future aging of silica sol, where the future aging degree calculation formula is: ,in, For the future time period, is the predicted total amount of light in the future time period, is the aging rate factor; According to the use requirements of silica sol, an aging failure threshold is set. When the aging degree reaches the aging failure threshold, the silica sol is aged to an unacceptable performance state. The remaining validity period of the silica sol is calculated based on the aging failure threshold and the future aging degree calculation formula. When the aging degree is greater than the aging failure threshold, the silica sol is marked as abnormal.

4. The method for evaluating the production quality of silica sol based on image analysis according to claim 3, wherein: The obtaining of silica sol image data and preprocessing of the obtained image include the following specific steps: After the silica sol sample is produced, microscopic images are collected at different storage time points to obtain ambient lighting data; The collected images are preprocessed in a standardized manner to remove background interference, enhance particle boundary features, convert the images into binary form, distinguish particle areas from background areas, extract each particle area from the binary image, identify all interconnected pixel areas as independent particle objects through connected domain labeling, and use contour extraction algorithm to identify the boundary pixels of each area.

5. A silica sol production quality assessment system based on image analysis, which is implemented based on the silica sol production quality assessment method based on image analysis according to any one of claims 1 to 4, characterized in that: Specifically include: A data acquisition module is used to acquire silica sol image data and pre-process the acquired image; The silica sol structure evolution module is used to calculate the silica sol particle structure characteristic parameters based on the silica sol particle parameters and evaluate the aging evolution degree of the silica sol. It includes the following specific steps: substituting the particle pixel area in the image into the silica sol particle size calculation formula to calculate the silica sol particle size, where the calculation formula for the j-th silica sol particle size is: ,in, is the actual pixel area of ​​the jth silica sol particle. The particle size of the silica sol particle is substituted into the average particle size calculation formula to calculate the average particle size value. The particle size and average particle size of the silica sol particle are substituted into the particle size distribution standard deviation calculation formula to evaluate the discreteness of the particles in the silica sol. The pixel area and perimeter of the silica sol particle are substituted into the particle average roundness calculation formula to evaluate the shape regularity of the particles in the silica sol. The particle average roundness calculation formula is: ,in, is the circumference of the jth particle, m is the total number of particles, the number of agglomerated and connected silica sol particles is substituted into the aggregation calculation formula to evaluate the adhesion between particles, where the aggregation is the ratio of the number of agglomerated and connected particles to the total number of particles, the particle size distribution standard deviation, particle average roundness and aggregation at the current moment are subtracted from the initial moment, and the obtained difference in particle size distribution standard deviation, particle average roundness and aggregation at the current moment and the initial moment are substituted into the aging calculation formula to evaluate the aging degree of silica sol, where the aging calculation formula is: ,in, 、 and are the particle size distribution weight, particle roundness weight and aggregation weight, respectively. is the standard deviation of the particle size distribution, is the average roundness difference of the particles, is the aggregation difference; Light layer attenuation module, used to calculate the total light dose of each layer through the incident light intensity and quantify the energy input of photoaging; Aging evolution module for evaluating future silica sol aging using total light dose.

6. An electronic device comprising: A processor and a memory, wherein the memory stores a computer program that can be called by the processor; It is characterized in that the processor executes the silica sol production quality assessment method based on image analysis according to any one of claims 1 to 4 by calling the computer program stored in the memory.

7. A computer-readable storage medium, characterized in that Instructions are stored, and when the instructions are run on a computer, the computer is caused to execute the silica sol production quality assessment method based on image analysis according to any one of claims 1 to 4.

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