A barley malting efficiency estimation method and system based on the method of peeling

Abstract

The present application relates to the technical field of data management, and specifically discloses a barley malting efficiency estimation method and system based on the peeling method, which comprises the following steps: obtaining and deeply analyzing barley malting quality parameters to estimate the barley malting efficiency once, and determining whether the peeling equipment needs to be adjusted once. Then, during the operation of the peeling equipment, the operation process is closely monitored, the bran residue parameters are collected and properly processed, and then it is determined whether to perform secondary adjustment. Finally, the abnormal parameters of the peeling equipment after secondary adjustment are obtained and accurately evaluated to determine whether a warning should be issued for the equipment operation. The method can comprehensively and dynamically control the barley malting process, and provides strong support for efficient and stable barley malting production through multi-stage parameter analysis and equipment adjustment decision, which has important significance for improving the production efficiency and quality of the malting industry.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data management, in particular to a barley malting efficiency estimation method and system based on the peeling method. BACKGROUND

[0002] The barley malting process based on the peeling method has attracted much attention in recent years due to its advantage of improving malt quality by removing bran. In traditional malting processes, barley bran can affect the subsequent fermentation process, increase the generation of harmful substances, and even reduce the malt extract rate. The peeling equipment can effectively solve these problems. However, the current process has many limitations in practical application. The physical properties of barley from different regions and varieties result in different peeling difficulties, affecting the universality of the peeling parameters of the equipment. On the other hand, the wear and tear of the equipment can also reduce the peeling efficiency. In addition, it is difficult to accurately control the process parameters of the equipment operation. These factors combined together make it difficult to achieve the ideal level of barley malting efficiency based on the peeling method of the peeling equipment. Therefore, there is an urgent need for technical innovation and optimization to improve efficiency and meet the growing demand of the industry.

[0003] For example, the invention patent with publication number CN118644153A discloses an agricultural product processing quality traceability monitoring system based on edge nodes, which relates to the field of agricultural product traceability technology. It includes a cloud monitoring platform, an edge node deployment module, a data transmission module, a blockchain construction module, a processing traceability module, and a processing execution module. The edge node deployment module deploys several edge device nodes and sensors on the agricultural product processing production line to obtain production line operation data. The data transmission module transmits the production line operation data in several edge device nodes. The blockchain construction module constructs corresponding blockchain nodes based on several production line operation data, and constructs an agricultural product traceability blockchain after node deployment and node testing of all blockchain nodes. The processing traceability module performs processing quality traceability on the agricultural product traceability blockchain. The processing execution module executes different production line processing operations according to the traceability results of the processing quality traceability.

[0004] For example, the invention patent with publication number CN117217615A discloses an agricultural product quality and safety production and processing method and system. The method includes: collecting agricultural product production and processing data; based on the agricultural product production and processing data, making agricultural product quality and safety inspection tasks and issuing them online; obtaining agricultural product quality and safety inspection results; based on the agricultural product quality and safety inspection results, tracing and solving the problems of agricultural product quality and safety.

[0005] However, in the process of implementing the embodiments of the present application, it is found that the above-mentioned technology at least has the following technical problems: the traditional peeling equipment adopts fixed parameters, which is difficult to adapt to different varieties and moisture content of barley raw materials, resulting in bran residue, frequent equipment jamming, and easy to cause breakage rate, current research focuses on single parameter optimization, lacks of synergistic regulation of peeling, chip removal and protection, which restricts the synchronous improvement of malting efficiency and quality. SUMMARY

[0006] In view of the deficiencies of the prior art, the present application provides a barley malting efficiency estimation method and system based on peeling method, which can effectively solve the problems involved in the above background art.

[0007] To achieve the above object, the present application is realized by the following technical scheme: the present application provides a barley malting efficiency estimation method based on peeling method, comprising: step one, obtaining and analyzing barley malting quality parameters, once estimating barley malting efficiency, and determining whether to adjust the peeling equipment once; step two, monitoring the running process of the peeling equipment, collecting and processing the bran residue parameters of the peeling equipment, and determining whether to adjust the peeling equipment twice; step three, obtaining and evaluating the abnormal parameters of the peeling equipment after the second adjustment, and determining whether to give a warning to the running of the peeling equipment.

[0008] As a further method, whether to adjust the peeling equipment once is determined, and the specific determination process is: comparing the malting quality coefficient with the malting quality coefficient threshold value, if the malting quality coefficient is greater than or equal to the malting quality coefficient threshold value, the peeling equipment does not need to be adjusted once, and the running process of the peeling equipment is monitored; if the malting quality coefficient is less than the malting quality coefficient threshold value, the peeling equipment needs to be adjusted once.

[0009] As a further method, the peeling equipment is adjusted once, and the specific adjustment process is: the malting quality coefficient is processed by difference with the malting quality coefficient threshold value, the processing result is processed by ratio with the malting quality coefficient threshold value, and finally the malting quality coefficient deviation value of the peeling equipment is obtained, based on the malting quality coefficient deviation value, the grinding wheel speed of the peeling equipment is increased and adjusted, and the electrostatic voltage of the peeling equipment is increased and adjusted.

[0010] As a further method, it is determined whether to make secondary adjustment to the skinning device, and the specific determination process is that the bran residue coefficient is subtracted from the bran residue coefficient threshold value, the processing result is divided by the bran residue coefficient threshold value, and finally the bran residue coefficient deviation value of the skinning device is obtained; the bran residue coefficient is compared with the bran residue coefficient threshold value, if the bran residue coefficient is greater than or equal to the bran residue coefficient threshold value, it is determined to make secondary adjustment to the skinning device, and the specific adjustment process is that the single skinning time is increased based on the bran residue deviation value, and the skinning times are increased based on the bran residue deviation value; if the bran residue coefficient is less than the bran residue coefficient threshold value, it is determined not to make secondary adjustment to the skinning device; and the single skinning time is reduced and optimized based on the bran residue coefficient deviation value.

[0011] As a further method, the skinning device is optimized three times based on the device jam warning index, and the specific optimization process is that the device jam warning index is compared with the device jam warning index reference interval, if the device jam warning index is less than the minimum value of the device jam warning index reference interval, the primary optimization measure is matched; if the device jam warning index belongs to the device jam warning index reference interval, the intermediate optimization measure is matched; if the device jam warning index is greater than the maximum value of the device jam warning index reference interval, the senior optimization measure is matched.

[0012] As a further method, it is determined whether to make a warning to the operation of the skinning device, and the specific determination process is that the malting quality secondary optimization coefficient is obtained, the malting quality secondary optimization coefficient is compared with the malting quality optimization coefficient threshold value, if the malting quality secondary optimization coefficient is greater than or equal to the malting quality coefficient threshold value, it is determined that the skinning device does not need to be warned; if the malting quality secondary optimization coefficient is less than the malting quality coefficient threshold value, it is determined that the skinning device needs to be warned.

[0013] The second aspect of the present application provides a barley malting efficiency estimation system based on skinning method, comprising: a data acquisition module for acquiring and analyzing barley malting quality parameters, estimating barley malting efficiency once, and determining whether to make primary adjustment to the skinning device; a determination module for monitoring the operation process of the skinning device, collecting and processing the bran residue parameters of the skinning device, and determining whether to make secondary adjustment to the skinning device; a warning module for acquiring and evaluating the abnormal parameters of the skinning device after secondary adjustment, and determining whether to make a warning to the operation of the skinning device.

[0014] Compared with the prior art, the embodiments of the present application have at least the following advantages or beneficial effects:

[0015] (1) The present application provides a barley malting efficiency estimation method and system based on the peeling method, dynamically obtains and analyzes the barley quality parameters and the peeling equipment operation data, realizes the accurate estimation of the malting efficiency and the real-time optimization adjustment of the equipment parameters, monitors the bran residue and the abnormal equipment parameters to early warn the potential risks, combines the data visualization and the intelligent decision support function, effectively improves the production efficiency, reduces the equipment loss, guarantees the production stability, considers the energy saving and emission reduction and environmental protection benefits, and finally realizes the intelligent, efficient and sustainable management of the malting process.

[0016] (2) The barley malting quality parameters are obtained and analyzed, the barley malting efficiency is estimated once, and it is determined whether to adjust the peeling equipment once, which has the advantages that the trend of the malting efficiency can be known in advance according to the key quality parameters at the initial stage of malting, the factors that may affect the efficiency are found in time through the comprehensive analysis of the previous malting situation, and then the first adjustment of the peeling equipment is made, so that the equipment can run in a state suitable for the barley from the beginning, laying a solid foundation for efficient malting, and avoiding the problem of low efficiency caused by the poor initial state of the equipment.

[0017] (3) The running process of the peeling equipment is monitored, the bran residue parameters of the peeling equipment are collected and processed, and it is determined whether to make a second adjustment to the peeling equipment, which has the advantages that the peeling link is tracked in real time, the running effect of the equipment is accurately grasped by focusing on the key indicator of bran residue, and the second adjustment can be made in time once the bran residue is found to be abnormal, which can minimize the interference of the bran residue on the subsequent malting process, optimize the peeling effect, improve the malting efficiency and malt quality, and guarantee the stable and efficient progress of the malting process. BRIEF DESCRIPTION OF DRAWINGS

[0018] The present application is further illustrated by the accompanying drawings, but the embodiments in the drawings do not constitute any limitation on the present application, and other drawings can be obtained by the following drawings without creative labor for ordinary skilled in the art.

[0019] Figure 1 The present application is further illustrated by the accompanying drawings, but the embodiments in the drawings do not constitute any limitation on the present application, and other drawings can be obtained by the following drawings without creative labor for ordinary skilled in the art.

[0020] Figure 2 The present application is further illustrated by the accompanying drawings, but the embodiments in the drawings do not constitute any limitation on the present application, and other drawings can be obtained by the following drawings without creative labor for ordinary skilled in the art.

[0021] Figure 3 The present application is further illustrated by the accompanying drawings, but the embodiments in the drawings do not constitute any limitation on the present application, and other drawings can be obtained by the following drawings without creative labor for ordinary skilled in the art.

[0022] Figure 4 The present application is further illustrated by the accompanying drawings, but the embodiments in the drawings do not constitute any limitation on the present application, and other drawings can be obtained by the following drawings without creative labor for ordinary skilled in the art. DETAILED DESCRIPTION

[0023] Clearly, the described embodiments are only a part of the embodiments of the present application, and not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative labor fall within the protection scope of the present application.

[0024] Referring to Figure 1 The first aspect of the present application provides a barley malting efficiency estimation method based on the peeling method, comprising: step one, obtaining and analyzing barley malting quality parameters, once estimating the barley malting efficiency, and determining whether to adjust the peeling equipment once.

[0025] Specifically, the barley malting quality parameters are obtained and analyzed, and the specific analysis process is: the barley malting quality parameters include the kernel brightness factor of the barley image, the kernel roughness factor of the barley image and the image entropy value factor of the barley image.

[0026] It should be explained that the kernel brightness factor of the above-mentioned barley image represents the proportional relationship between the kernel brightness of the barley image and the kernel brightness limit value; the kernel roughness factor of the above-mentioned barley image represents the proportional relationship between the kernel roughness of the barley image and the kernel roughness limit value; and the image entropy value factor of the above-mentioned barley image represents the proportional relationship between the image entropy value of the barley image and the image entropy limit value.

[0027] By introducing the influence coefficient, the influence degree of the kernel brightness factor of the barley image on the malting quality coefficient, the influence degree of the kernel roughness factor of the barley image on the malting quality coefficient and the influence degree of the image entropy value factor of the barley image on the malting quality coefficient are quantified respectively, and the influence degrees are coupled to obtain the malting quality coefficient, wherein the malting quality coefficient represents the peeling effect of the barley in the malting process, and the specific evaluation method is:

[0028] ;

[0029] ;

[0030] ;

[0031] ;

[0032] In the formula, is the malting quality coefficient of the peeling equipment, is the kernel brightness factor of the barley image, is the kernel brightness of the barley image, is the preset kernel brightness limit value in the equipment database, is the kernel roughness factor of the barley image, a kernel roughness of the barley image, a preset kernel roughness limit value in the equipment database, an image entropy value factor of the barley image, an image entropy value of the barley image, a preset image entropy limit value in the equipment database, a weight coefficient corresponding to the kernel brightness factor preset in the equipment database, a weight coefficient corresponding to the kernel roughness factor preset in the equipment database, a weight coefficient corresponding to the image entropy value factor preset in the equipment database.

[0033] It should be explained that the kernel brightness of the barley image represents the brightness of the kernel surface, which is obtained by extracting the kernel region through image segmentation (such as threshold method) and calculating the average gray value of the kernel region; the kernel roughness of the barley image is used to describe the texture characteristics of the kernel surface, which represents the smoothness or roughness of the kernel surface, which is obtained by using a gray level co-occurrence matrix; and the image entropy value of the barley image represents the richness of image information, which is calculated by a Shannon entropy formula.

[0034] The kernel brightness limit value represents the maximum kernel brightness value allowed in the equipment database; the kernel roughness limit value represents the maximum kernel roughness value allowed in the equipment database; and the image entropy limit value represents the maximum image entropy value allowed in the equipment database.

[0035] The weight coefficient corresponding to the kernel brightness factor represents the influence degree of the kernel brightness unit value on the milling quality coefficient of the peeling equipment; the weight coefficient corresponding to the kernel roughness factor represents the influence degree of the kernel roughness unit value on the milling quality coefficient of the peeling equipment; and the weight coefficient corresponding to the image entropy value factor represents the influence degree of the image entropy unit value on the milling quality coefficient of the peeling equipment; the equipment database stores the corresponding relationship between the kernel brightness factor and the corresponding kernel brightness factor weight coefficient, the corresponding relationship between the kernel roughness factor and the corresponding kernel roughness factor weight coefficient, and the corresponding relationship between the image entropy value factor and the corresponding image entropy value factor weight coefficient, for example, the kernel brightness factor, the kernel roughness factor and the image entropy value factor are input into the equipment database, and the equipment database can match the kernel brightness factor weight coefficient, the kernel roughness factor weight coefficient and the image entropy value factor weight coefficient, the value range of which is between 0 and 1.

[0036] Generally, the higher the brightness of the wheat kernel, the smoother the surface of the wheat kernel, and the lower the roughness. The higher the roughness of the wheat kernel means that the texture and the gray scale change of the surface of the wheat kernel are more abundant, and the more information the image contains, and the image entropy value is correspondingly increased. If there is a large difference in the brightness of the wheat kernel, that is, part of the wheat kernel is very bright and part of the wheat kernel is relatively dark, then the gray scale distribution of the image is more extensive, and the image entropy value is increased.

[0037] The higher the brightness of the wheat kernel means that the wheat kernel has undergone good peeling treatment, thereby positively affecting the malting quality and improving the malting quality coefficient. The surface of the wheat kernel with lower roughness is relatively smooth, and has less impurities and damage, which is beneficial to the formation of uniform malt quality, thereby improving the malting quality coefficient. The low image entropy value indicates that the gray scale distribution of the barley image is relatively concentrated, and the characteristics of the wheat kernel are relatively single, which indicates that the consistency and uniformity of the barley are good, and the malting quality coefficient can be improved.

[0038] Specifically, whether to make an adjustment to the peeling equipment is determined by comparing the malting quality coefficient with a malting quality coefficient threshold value. If the malting quality coefficient is greater than or equal to the malting quality coefficient threshold value, no adjustment is needed to the peeling equipment, and the running process of the peeling equipment is monitored. If the malting quality coefficient is less than the malting quality coefficient threshold value, an adjustment is needed to the peeling equipment.

[0039] It should be explained that the above-mentioned malting quality coefficient threshold value represents the minimum value of the malting quality coefficient allowed in the equipment database.

[0040] Further, the adjustment to the peeling equipment is made by performing difference processing on the malting quality coefficient threshold value and the malting quality coefficient, performing ratio processing on the processing result and the malting quality coefficient threshold value, and finally obtaining a malting quality coefficient deviation value of the peeling equipment. Based on the malting quality coefficient deviation value, the rotational speed of the grinding wheel of the peeling equipment is adjusted to increase, and the electrostatic voltage of the peeling equipment is adjusted to increase.

[0041] It should be explained that the above-mentioned adjustment to increase the rotational speed of the grinding wheel of the peeling equipment and to increase the electrostatic voltage of the peeling equipment is specifically adjusted by: storing a grinding wheel rotational speed increase coefficient corresponding to each malting quality coefficient deviation value interval in the equipment database, inputting the obtained malting quality coefficient deviation value into the equipment database, automatically matching the interval corresponding to the malting quality coefficient deviation value, and then the increase coefficient corresponding to the interval is the increase coefficient (greater than 1) of the rotational speed of the grinding wheel. The increase coefficient of the rotational speed of the grinding wheel is multiplied by the original rotational speed of the grinding wheel, and the obtained result is the adjusted rotational speed of the grinding wheel. The above-mentioned increase coefficient of the rotational speed of the grinding wheel represents the numerical value of the multiple of the increase of the rotational speed of the grinding wheel.

[0042] Similarly, the device database stores the increase coefficient of the electrostatic voltage corresponding to each malt quality coefficient deviation value interval, inputs the obtained malt quality coefficient deviation value into the device database, automatically matches the interval corresponding to the malt quality coefficient deviation value, and the increase coefficient corresponding to the interval is the increase coefficient (greater than 1) of the electrostatic voltage. Multiply the original electrostatic voltage by the increase coefficient of the electrostatic voltage, and the result obtained is the electrostatic voltage that needs to be adjusted. The increase coefficient of the electrostatic voltage represents the numerical value of the multiple of the electrostatic voltage that needs to be increased.

[0043] In a specific embodiment, the malt quality parameters of the barley are obtained and analyzed, the malt efficiency of the barley is estimated once, and it is determined whether to adjust the peeling equipment once. The advantage is that the trend of the malt efficiency can be predicted in advance based on the key quality parameters at the beginning of the malt making, the factors that may affect the efficiency are found in time through comprehensive research and judgment of the early malt making, and the peeling equipment is adjusted for the first time, so that the equipment runs in a state suitable for the barley from the beginning, lays a foundation for efficient malt making, and avoids the problem of low efficiency caused by the poor initial state of the equipment.

[0044] Step two, monitoring the running process of the peeling equipment, collecting and processing the bran residue parameters of the peeling equipment, and determining whether to adjust the peeling equipment for the second time.

[0045] Specifically, the bran residue parameters of the peeling equipment are collected and processed, and the specific processing process is as follows: the bran residue parameters of the peeling equipment include the screen mesh aperture factor of the peeling equipment, the impact energy density factor of the peeling equipment, and the effective friction frequency factor of the peeling equipment.

[0046] It needs to be explained that the screen mesh aperture factor of the peeling equipment represents the proportional relationship between the deviation of the screen mesh aperture size of the peeling equipment and the screen mesh aperture reference value and the screen mesh aperture reference value; the impact energy density factor of the peeling equipment represents the proportional relationship between the impact energy density of the peeling equipment and the impact energy density defined value; and the effective friction frequency factor of the peeling equipment represents the proportional relationship between the deviation of the effective friction frequency of the peeling equipment and the effective friction frequency reference value and the effective friction frequency reference value.

[0047] By introducing the influence coefficient, the influence degree of the screen mesh aperture factor of the peeling equipment on the bran residue parameter, the influence degree of the impact energy density factor of the peeling equipment on the bran residue parameter, the influence degree of the effective friction frequency factor of the peeling equipment on the bran residue parameter, and the influence degree of the malt quality optimization coefficient on the bran residue parameter are quantified, and the influence degrees are coupled to obtain the bran residue coefficient, wherein the bran residue coefficient represents the residual degree of the surface bran of the barley after peeling, and the specific evaluation method is as follows:

[0048] ;

[0049] ;

[0050] ;

[0051] ;

[0052] wherein, is a bran residue coefficient of the bran stripping device, is a malting quality optimization coefficient, is a screen aperture factor of the bran stripping device, is a screen aperture size of the bran stripping device, is a preset screen aperture reference value in the device database, is an impact energy density factor of the bran stripping device, is an impact energy density of the bran stripping device, is a preset impact energy density limit value in the device database, is an effective friction times factor of the bran stripping device, is an effective friction times of the bran stripping device, is a preset effective friction times reference value in the device database, is a weight coefficient corresponding to the screen aperture factor in the device database, is a weight coefficient corresponding to the impact energy density factor in the device database, is a weight coefficient corresponding to the effective friction times factor in the device database, is a weight coefficient corresponding to the malting quality optimization coefficient in the device database.

[0053] It needs to be explained that the above-mentioned malting quality optimization coefficient refers to the malting quality coefficient during the target duration after the first adjustment and is marked as the malting quality optimization coefficient, wherein the target duration refers to the preset monitoring duration in the database; the above-mentioned screen aperture size refers to the diameter size of the holes on the screen, which can be measured by a laser diameter measuring instrument; the above-mentioned impact energy density refers to the distribution of the impact force on the unit area received by the grains, which can be measured by a pressure sensor; the above-mentioned effective friction times refers to the number of times of effective friction between the grains and the friction component (such as a grinding wheel, etc.), which can be obtained by a friction and wear testing machine, and the above-mentioned effective friction can be indirectly judged by monitoring the power consumption or current change of the bran stripping device; when the device performs friction stripping on the grains, if the power or current rises, it means that the device overcomes the friction force to do work and consumes energy for the friction between the grains and the friction component, so it can be inferred that effective friction occurs.

[0054] The reference value of the screen mesh size refers to a reference value of the screen mesh size stored in the equipment database; the impact energy density limit value refers to a maximum value of the impact energy density allowed in the equipment database; and the effective friction times reference value refers to a reference value of the effective friction times stored in the equipment database.

[0055] The weight coefficient corresponding to the screen mesh size factor refers to the influence degree of the unit value change of the screen mesh size on the bran residue coefficient; the weight coefficient corresponding to the impact energy density factor refers to the influence degree of the unit value change of the impact energy density on the bran residue coefficient; the weight coefficient corresponding to the effective friction times factor refers to the influence degree of the unit value change of the effective friction times on the bran residue coefficient; the weight coefficient corresponding to the malting quality optimization coefficient refers to the influence degree of the unit value change of the malting quality optimization coefficient on the bran residue coefficient; and the corresponding relationship between the screen mesh size factor and the weight coefficient corresponding to the screen mesh size factor, the corresponding relationship between the impact energy density factor and the weight coefficient corresponding to the impact energy density factor, the corresponding relationship between the effective friction times factor and the weight coefficient corresponding to the effective friction times factor, and the corresponding relationship between the malting quality optimization coefficient and the weight coefficient corresponding to the malting quality optimization coefficient are stored in the equipment database, for example, when the screen mesh size factor, the impact energy density factor, the effective friction times factor and the malting quality optimization coefficient are input into the equipment database, the equipment database can match the weight coefficient corresponding to the screen mesh size factor, the weight coefficient corresponding to the impact energy density factor, the weight coefficient corresponding to the effective friction times factor and the weight coefficient corresponding to the malting quality optimization coefficient, and the value range is between 0 and 1.

[0056] A smaller screen mesh size can limit the bran passing through, and a higher impact energy density is required to break the bran to avoid clogging, which can also cause more effective friction times, and can also cause the passing rate of the malt to decrease, the production efficiency to decrease, and the malting quality optimization coefficient to be adversely affected; a higher impact energy density can sufficiently break the bran, reduce the required friction times, and a suitable impact energy density can increase the germination rate of the malt and the enzyme activity, thereby increasing the malting quality optimization coefficient; and the increase of the effective friction times can cause the screen mesh size to gradually increase, which can affect the screening effect.

[0057] The larger the screen mesh size is, the larger and more bran particles can pass through the screen, which can increase the bran residue coefficient; appropriately increasing the impact energy density can increase the impact force on the malt, which can make the bran more easily fall off from the surface of the malt, thereby reducing the bran residue coefficient in the equipment; increasing the effective friction times can make the bran and the malt separate more completely, thereby reducing the bran residue coefficient; and the higher the malting quality optimization coefficient is, the smaller the bran residue coefficient of the equipment is.

[0058] Further, it is determined whether to make secondary adjustment to the bran stripping device. The specific determination process is that the bran residue coefficient is subtracted from the bran residue coefficient threshold value, and the processing result is divided by the bran residue coefficient threshold value, and finally the bran residue coefficient deviation value of the bran stripping device is obtained.

[0059] It should be explained that the bran residue coefficient threshold value represents the maximum value of the bran residue coefficient allowed in the device database; the bran residue coefficient deviation value represents the numerical value of the deviation of the bran residue condition from the expected standard.

[0060] The bran residue coefficient is compared with the bran residue coefficient threshold value. If the bran residue coefficient is greater than or equal to the bran residue coefficient threshold value, it is determined to make secondary adjustment to the bran stripping device. Specifically, the single bran stripping time is increased based on the bran residue deviation value, and the number of bran stripping is increased based on the bran residue deviation value.

[0061] It should be explained that the single bran stripping time is increased based on the bran residue deviation value. The specific adjustment process is that the device database stores adjustment coefficients corresponding to each bran residue deviation value interval. The obtained bran residue deviation value is input into the device database, and the interval corresponding to the bran residue deviation value is automatically matched. The adjustment coefficient corresponding to the interval is the single bran stripping time adjustment coefficient (the single bran stripping time adjustment coefficient is greater than 1). The single bran stripping time adjustment coefficient is multiplied by the original single bran stripping time, and the result obtained is the adjusted single bran stripping time. The number of bran stripping is increased based on the bran residue deviation value. The specific adjustment process is that the device database stores increase times corresponding to each bran residue deviation value interval. The obtained bran residue deviation value is input into the device database, and the interval corresponding to the bran residue deviation value is automatically matched. The increase times corresponding to the interval is the increase times of the number of bran stripping. The number of bran stripping is increased by the increase times corresponding to the bran residue deviation value on the basis of the original number of bran stripping. It should be noted that the number of bran stripping cannot exceed the maximum number of bran stripping, so as to avoid damage to the wheat kernels, loss of nutrients and too long production process. If the adjustment result of the number of bran stripping is greater than the maximum number of bran stripping, the final adjustment result is the maximum number of bran stripping.

[0062] If the bran residue coefficient is less than the bran residue coefficient threshold value, it is determined not to make secondary adjustment to the bran stripping device. At the same time, the single bran stripping time is reduced and optimized based on the absolute value of the bran residue coefficient deviation value.

[0063] It needs to be explained that the absolute value of the bran residue coefficient deviation value is used to reduce and optimize the single peeling time. The specific optimization process is: the device database stores the adjustment coefficient corresponding to the absolute value interval of each bran residue deviation value. The absolute value of the bran residue deviation value obtained is input into the device database, and the interval corresponding to the absolute value of the bran residue deviation value is automatically matched. The adjustment coefficient corresponding to the interval is the single peeling time adjustment coefficient (the single peeling time adjustment coefficient is less than 1). Multiply the original single peeling time by the single peeling time adjustment coefficient, and the result is the adjusted single peeling time. The single peeling time adjustment coefficient refers to the numerical value of the single peeling time adjustment multiple.

[0064] In a specific embodiment, the operation process of the peeling device is monitored, the bran residue parameters of the peeling device are collected and processed, and it is determined whether to make a secondary adjustment to the peeling device. The advantage of this step is to track the peeling process in real time, accurately grasp the operation effect of the device by focusing on the key indicator of bran residue, and make a secondary adjustment in time once the bran residue is found to be abnormal. This can minimize the interference of bran residue on the subsequent malting process, optimize the peeling effect, improve the malting efficiency and malt quality, and ensure the stability and efficiency of the malting process.

[0065] Step three, obtain and evaluate the abnormal parameters of the peeling device after secondary adjustment, and determine whether to make a pre-warning to the operation of the peeling device.

[0066] Specifically, the abnormal parameters of the peeling device after secondary adjustment are obtained and evaluated. The specific evaluation process is: the abnormal parameters of the peeling device after secondary adjustment include the vibration amplitude factor of the peeling device, the sound intensity factor of the peeling device, the malting quality optimization coefficient, and the secondary bran residue coefficient of the peeling device.

[0067] It needs to be explained that the vibration amplitude factor of the peeling device represents the proportional relationship between the vibration amplitude of the peeling device and the vibration amplitude limit value; the sound intensity factor of the peeling device represents the proportional relationship between the sound intensity of the peeling device and the sound intensity limit value; and the secondary bran residue coefficient refers to the bran residue coefficient of the device within a short period of time after secondary adjustment and is marked as the secondary bran residue coefficient.

[0068] The influence degree of the vibration amplitude factor of the dehulling equipment on the equipment jam early warning index, the influence degree of the sound intensity factor of the dehulling equipment on the equipment jam early warning index, the influence degree of the malt quality optimization coefficient on the equipment jam early warning index and the influence degree of the secondary malt residue coefficient of the dehulling equipment on the equipment jam early warning index are quantified by introducing influence coefficients, the influence degrees are coupled, and finally the equipment jam early warning index is obtained, so that the dehulling equipment is optimized three times based on the equipment jam early warning index, wherein the equipment jam early warning index represents the possibility of the dehulling equipment to appear a jam fault, and the specific evaluation method is:

[0069] ;

[0070] ;

[0071] ;

[0072] In the formula, is the equipment jam early warning index, is the malt quality optimization coefficient, is the secondary malt residue coefficient of the dehulling equipment, is the vibration amplitude factor of the dehulling equipment, is the vibration amplitude of the dehulling equipment, is the preset vibration amplitude limit value in the equipment database, is the sound intensity factor of the dehulling equipment, is the sound intensity of the dehulling equipment, is the preset sound intensity limit value in the equipment database, is the weight coefficient corresponding to the preset vibration amplitude factor in the equipment database, is the weight coefficient corresponding to the preset sound intensity factor in the equipment database, is the weight coefficient corresponding to the preset malt quality optimization coefficient in the equipment database, is the weight coefficient corresponding to the preset secondary malt residue coefficient in the equipment database.

[0073] It should be explained that the vibration amplitude of the dehulling equipment represents the severity of equipment vibration, and the vibration amplitude information can be obtained by a vibration sensor; the sound intensity of the dehulling equipment represents the loudness of the sound emitted during equipment operation, and the sound intensity can be obtained by a sound sensor.

[0074] The vibration amplitude limiting value represents the maximum vibration amplitude allowed in the equipment database; the sound intensity limiting value represents the maximum sound intensity allowed in the equipment database; the weight coefficient corresponding to the vibration amplitude factor represents the influence degree of the unit value of the vibration amplitude on the equipment jam warning index; the weight coefficient corresponding to the sound intensity factor represents the influence degree of the unit value of the sound intensity on the equipment jam warning index; the weight coefficient corresponding to the malt quality optimization coefficient represents the influence degree of the unit value of the malt quality optimization coefficient on the equipment jam warning index; the weight coefficient corresponding to the secondary bran residue coefficient represents the influence degree of the unit value of the secondary bran residue coefficient on the equipment jam warning index; the equipment database stores the corresponding relationship between the vibration amplitude factor and the vibration amplitude factor weight coefficient corresponding thereto, the corresponding relationship between the sound intensity factor and the sound intensity factor weight coefficient corresponding thereto, the corresponding relationship between the vibration malt quality optimization coefficient and the malt quality optimization coefficient weight coefficient corresponding thereto, and the corresponding relationship between the secondary bran residue coefficient and the secondary bran residue coefficient weight coefficient corresponding thereto, for example, inputting the vibration amplitude factor, the sound intensity factor, the malt quality optimization coefficient, and the secondary bran residue coefficient into the equipment database, and the equipment database can match the vibration amplitude factor weight coefficient, the sound intensity factor weight coefficient, the malt quality optimization coefficient weight coefficient, and the secondary bran residue coefficient weight coefficient, the value range of which is between 0 and 1.

[0075] The greater the vibration amplitude of the equipment, the greater the sound intensity it produces, because greater vibration will cause more intense collision and friction of the equipment components, thereby producing stronger sound; the abnormal increase of the vibration amplitude of the equipment may mean that the running state of the equipment is unstable, which may affect the peeling effect, causing the secondary bran residue coefficient to change; the change of the sound intensity often reflects the working state inside the equipment, when the sound intensity abnormally increases, it may be that there is a blockage or jam inside the equipment, which will affect the normal flow of the malt and the peeling process, thereby causing the secondary bran residue coefficient to rise; the abnormal increase of the vibration amplitude and the sound intensity of the equipment often means that the equipment has a problem in running, which may have a negative impact on the malt quality, causing the malt quality optimization coefficient to decrease, and the secondary bran residue coefficient to rise, indicating that the peeling effect is poor, which will also affect the malt quality to some extent, causing the malt quality optimization coefficient to decrease.

[0076] The greater the vibration amplitude, the more likely the equipment will have a jam or other faults, thereby causing the equipment jam warning index to rise; the greater the sound intensity, the higher the possibility of the equipment jam, and the equipment warning index will also rise; the higher the malt quality optimization coefficient, the better the running state of the equipment, the lower the possibility of the equipment jam, and the lower the equipment jam warning index; the increase of the secondary bran residue coefficient will usually cause the equipment jam warning index to rise.

[0077] Further, based on the device jam warning index, the peeling device is optimized three times. The specific optimization process is: comparing the device jam warning index with the device jam warning index reference interval, if the device jam warning index is less than the minimum value of the device jam warning index reference interval, the primary optimization measure is matched; if the device jam warning index belongs to the device jam warning index reference interval, the intermediate optimization measure is matched; if the device jam warning index is greater than the maximum value of the device jam warning index reference interval, the advanced optimization measure is matched.

[0078] It needs to be explained that the above-mentioned device jam warning index reference interval represents the numerical range preset in the device database for evaluating the risk degree of device jam; the above-mentioned matching primary optimization measure has the following specific matching process: the device database stores the device jam warning index and its corresponding device optimization measure, for example, the device jam warning index is input into the device database, the device database compares the device jam warning index with the device jam warning index reference interval, and the corresponding optimization measure is matched according to the comparison result.

[0079] It needs to be explained that when the device jam warning index is less than the minimum value of the device jam warning index reference interval, the jam risk is low, and the primary optimization measure is matched, the specific content is: on the basis of the original, the gap between the grinding wheel is increased millimeters This fine tuning can not only reduce the contact area between the grinding wheel and the material to a certain extent and reduce the friction resistance, but also will not have a significant impact on the normal processing precision and efficiency of the device; the wind speed is increased meters per second A small increase in wind speed can timely blow away the small amount of debris and dust in the device without consuming too much energy, keep the device clean and prevent the gradual accumulation of debris and dust from causing jam.

[0080] When the device jam warning index belongs to the device jam warning index reference interval, the jam risk increases, and the intermediate optimization measure is matched, the specific content is: on the basis of the original, the gap between the grinding wheel is increased millimeters, this numerical adjustment can reduce the friction resistance between the grinding wheel and the material to a greater extent under the premise that the device can still work normally, which helps to reduce the heat and debris generated by friction, thereby effectively reducing the jam risk; the wind speed is increased meters per second, a larger increase in wind speed can more effectively remove the debris and dust in the grinding wheel area, accelerate air circulation, reduce the risk of blockage, and also help to remove the heat generated during device operation, improving the operating environment of the device.​​​​

[0081] When the equipment jam early warning index is greater than the maximum value of the equipment jam early warning index reference interval, the jam risk is extremely high, and a high-level optimization measure is matched, and the specific content is: on the basis of the original, the grinding wheel gap is increased mm, the direct contact between the grinding wheel and the barley is reduced to the maximum extent, and the gap is adjusted to a larger range without affecting the basic function of the equipment, so as to rapidly reduce the frictional resistance, avoid excessive extrusion and blockage of the material in the grinding wheel area, and effectively prevent the equipment from jamming; the wind speed is increased m / s, which can generate sufficient wind power to quickly remove the debris accumulated in the equipment, prevent further blockage of the equipment, and ensure smooth circulation of the material and air in the equipment; at the same time, the auxiliary chip removal device (such as pulse airflow blowing) is started, which can more thoroughly remove the debris in the equipment, especially the debris attached to the inner wall of the equipment or difficult to be blown away by conventional wind speed, further reducing the jamming risk and ensuring the normal operation of the equipment.

[0082] Specifically, whether to perform early warning on the operation of the peeling equipment, and the specific determination process is: obtaining the second optimization coefficient of the milling quality, comparing the second optimization coefficient of the milling quality with the threshold value of the milling quality optimization coefficient, if the second optimization coefficient of the milling quality is greater than or equal to the threshold value of the milling quality coefficient, it is determined that no early warning is needed for the peeling equipment; if the second optimization coefficient of the milling quality is less than the threshold value of the milling quality coefficient, it is determined that early warning is needed for the peeling equipment.

[0083] It needs to be explained that the above-mentioned second optimization coefficient of the milling quality refers to the milling quality coefficient in the target monitoring time period after the third optimization and is marked as the second optimization coefficient of the milling quality.

[0084] In one specific embodiment, the present application provides a barley milling efficiency estimation method based on the peeling method, dynamically obtains and analyzes the barley quality parameters and the peeling equipment operation data, realizes accurate estimation of the milling efficiency and real-time optimization adjustment of the equipment parameters, simultaneously monitors the bran residue and the abnormal parameters of the equipment to early warn potential risks, combines data visualization and intelligent decision support functions, effectively improves production efficiency, reduces equipment wear and tear, ensures production stability, and takes into account energy saving and emission reduction and environmental protection benefits, and finally realizes intelligent, efficient and sustainable management of the milling process; the detailed process is as shown in Figure 2 First, the barley milling quality parameters are obtained and the milling quality coefficient is calculated, which is compared with the threshold value, if it is less than the threshold value, the deviation value is calculated and the grinding wheel speed and the electrostatic voltage are adjusted, otherwise the equipment operation is directly monitored; then the bran residue parameters are collected to calculate the bran residue coefficient, which is compared with the threshold value, if it is greater than or equal to the threshold value, the deviation value is calculated and the single peeling time and frequency are adjusted, if it is less than the threshold value, the single peeling time is reduced; then the abnormal parameters are obtained to calculate the equipment jam early warning index; thenFigure 3 As shown: comparison of exponential with interval minimum value and interval range, respectively match primary, intermediate or high level optimization measures; finally obtain the secondary optimization coefficient of malting quality, compare with threshold value, less than threshold value, then pre-warning for equipment, greater than or equal to threshold value, no need to pre-warning, thus complete the whole malting efficiency estimation and equipment state management process.

[0085] Referring to Figure 4 As shown, the second aspect of the present application provides a barley malting efficiency estimation system based on the peeling method, comprising: a data acquisition module, a determination module, a warning module and a device database.

[0086] The data acquisition module is connected with the determination module, the determination module is connected with the warning module, and the data acquisition module, the determination module and the warning module are jointly connected with the database.

[0087] The data acquisition module is used for acquiring and analyzing the barley malting quality parameters, estimating the barley malting efficiency once, and determining whether to adjust the peeling equipment once; the determination module is used for monitoring the running process of the peeling equipment, collecting and processing the bran residue parameters of the peeling equipment, and determining whether to adjust the peeling equipment twice; the warning module is used for acquiring and evaluating the abnormal parameters of the peeling equipment after the secondary adjustment, and determining whether to pre-warning for the running of the peeling equipment.

[0088] The above content is only an example and description of the structure of the present application, and those skilled in the art can make various modifications or supplements or use similar ways to replace the described specific embodiments, as long as they do not deviate from the structure of the present application or exceed the scope defined by the present application, which shall belong to the protection scope of the present application.

Claims

1. A method for predicting barley processing efficiency based on the hulling method, characterized in that, include: Step 1: Obtain and analyze barley processing quality parameters, estimate barley processing efficiency once, and determine whether to adjust the hulling equipment. The barley processing quality parameters include the barley grain brightness factor, the barley grain roughness factor, and the barley image entropy factor. By introducing influence coefficients to quantify the influence of barley grain brightness factor, barley grain roughness factor, and barley image entropy factor on barley quality coefficient, respectively, the influence of each influence factor is coupled to obtain barley quality coefficient, where barley quality coefficient represents the hulling effect of barley in the barley processing process. Step 2: Monitor the operation of the peeling equipment, collect and process the bran residue parameters of the peeling equipment, and determine whether the peeling equipment needs to be adjusted again. The bran residue parameters of the peeling equipment include the screen aperture factor of the peeling equipment, the impact energy density factor of the peeling equipment, and the effective friction number factor of the peeling equipment. By introducing influence coefficients, the influence of the screen aperture factor of the hulling equipment on the bran residue parameters, the influence of the impact energy density factor of the hulling equipment on the bran residue parameters, the influence of the effective friction number factor of the hulling equipment on the bran residue parameters, and the influence of the wheat quality optimization coefficient on the bran residue parameters are quantified respectively. The influence of each factor is coupled to obtain the bran residue coefficient, which represents the degree of bran residue on the surface of barley after hulling. Step 3: Obtain and evaluate the abnormal parameters of the peeling equipment after the second adjustment, and determine whether to issue an early warning for the operation of the peeling equipment; the abnormal parameters of the peeling equipment after the second adjustment include the vibration amplitude factor of the peeling equipment, the sound intensity factor of the peeling equipment, the wheat quality optimization coefficient, and the secondary bran residue coefficient of the peeling equipment. By introducing influence coefficients, the influence of the vibration amplitude factor of the hulling equipment, the sound intensity factor of the hulling equipment, the wheat processing quality optimization coefficient, and the secondary bran residue coefficient of the hulling equipment on the hulling equipment jamming warning index are quantified. The influence of each factor is coupled to obtain the hulling equipment jamming warning index. Based on the hulling equipment jamming warning index, the hulling equipment is optimized three times. The hulling equipment jamming warning index represents the probability of jamming failure in the hulling equipment.

2. The method for predicting barley processing efficiency based on the hulling method according to claim 1, characterized in that: The specific process for determining whether to adjust the peeling equipment is as follows: The wheat processing quality coefficient is compared with the wheat processing quality coefficient threshold. If the wheat processing quality coefficient is greater than or equal to the wheat processing quality coefficient threshold, no adjustment is needed to the peeling equipment, and the operation of the peeling equipment is monitored. If the wheat processing quality coefficient is less than the wheat processing quality coefficient threshold, the peeling equipment needs to be adjusted.

3. The method for predicting barley processing efficiency based on the hulling method according to claim 2, characterized in that: The process of adjusting the peeling equipment is as follows: The difference between the wheat quality coefficient threshold and the wheat quality coefficient is processed, and the result is compared with the wheat quality coefficient threshold to obtain the deviation value of the wheat quality coefficient of the peeling equipment. Based on the deviation value of the wheat quality coefficient, the grinding wheel speed and the electrostatic voltage of the peeling equipment are increased.

4. The method for predicting barley processing efficiency based on the hulling method according to claim 1, characterized in that: The specific process for determining whether the peeling equipment needs to be adjusted again is as follows: The difference between the bran residue coefficient and the bran residue coefficient threshold is processed, and the processing result is compared with the bran residue coefficient threshold to finally obtain the bran residue coefficient deviation value of the dehulling equipment. The bran residue coefficient is compared with the bran residue coefficient threshold. If the bran residue coefficient is greater than or equal to the bran residue coefficient threshold, it is determined that the peeling equipment should be adjusted a second time. Specifically, the peeling time per session is increased based on the bran residue deviation value, and the number of peeling sessions is also increased based on the bran residue deviation value. If the bran residue coefficient is less than the bran residue coefficient threshold, it is determined that no secondary adjustment will be made to the peeling equipment; at the same time, the single peeling time will be optimized by reducing the absolute value of the bran residue coefficient deviation.

5. The method for predicting barley processing efficiency based on the hulling method according to claim 1, characterized in that: The peeling equipment was optimized three times based on the equipment jamming early warning index. The specific optimization process is as follows: The equipment jamming warning index is compared with the equipment jamming warning index reference range. If the equipment jamming warning index is less than the minimum value of the equipment jamming warning index reference range, then a primary optimization measure is matched. If the equipment jamming warning index falls within the equipment jamming warning index reference range, then a medium-level optimization measure will be applied. If the equipment jamming warning index is greater than the maximum value of the equipment jamming warning index reference range, then advanced optimization measures will be applied.

6. The method for predicting barley processing efficiency based on the hulling method according to claim 1, characterized in that: The specific determination process for whether to issue an early warning for the operation of the peeling equipment is as follows: Obtain the secondary optimization coefficient of wheat processing quality, compare the secondary optimization coefficient of wheat processing quality with the threshold of wheat processing quality optimization coefficient, and if the secondary optimization coefficient of wheat processing quality is greater than or equal to the threshold of wheat processing quality coefficient, it is determined that there is no need to issue an early warning for the peeling equipment. If the secondary optimization coefficient of wheat processing quality is less than the threshold of wheat processing quality coefficient, it is determined that an early warning should be issued for the peeling equipment.

7. A system for predicting barley processing efficiency based on the hulling method as described in any one of claims 1-6, characterized in that: include: The data acquisition module is used to acquire and analyze barley processing quality parameters, estimate barley processing efficiency once, and determine whether to make adjustments to the hulling equipment. The judgment module is used to monitor the operation of the peeling equipment, collect and process the bran residue parameters of the peeling equipment, and determine whether the peeling equipment needs to be adjusted again. The early warning module is used to acquire and evaluate abnormal parameters of the peeling equipment after secondary adjustments, and to determine whether to issue an early warning for the operation of the peeling equipment.

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