Intelligent Integrated Management System for Dairy Beverage Production Line

Abstract

The present invention discloses an intelligent integrated management system for a dairy beverage production line, which relates to the technical field of dairy beverages and includes the following steps: obtaining the operating environment and component data of the dairy beverage production line to be detected, and constructing a geometric model of dairy equipment based on 3D modeling software; obtaining a number of different injection pressures to be screened; respectively performing solvent leakage detection, and obtaining leakage path data and solvent and equipment loss data during solvent leakage detection; constructing an injection pressure evaluation model; constructing a pressure-detection effect display model, and performing data analysis to obtain the screened injection pressure, which is applied to the injection pressure control of intelligent solvent leakage detection of the dairy beverage production line, so as to solve the problems that in the intelligent solvent leakage detection of the dairy beverage production line in the prior art, the injection pressure control is often inaccurate, resulting in insufficient sensitivity of the intelligent control of production lines of different equipment or equipment damage.

Description

Technical Field

[0001] The present invention relates to the technical field of dairy beverages, and more specifically, to an intelligent integrated management system for a dairy beverage production line. Background Art

[0002] The dairy beverage field is a highly competitive and technology-intensive industry, covering a variety of products such as milk, yogurt, cheese, and lactic acid beverages. With the improvement of consumers' health awareness, the types and functions of dairy beverages have also become increasingly diverse, and new products rich in probiotics, low sugar, low fat, etc. have emerged to meet the needs of different consumers. To ensure product quality and safety, the production of dairy beverages requires highly automated mechanical equipment, and all links from raw material processing to packaging rely on precise mechanical systems.

[0003] The intelligent integrated management system of a dairy beverage production line realizes comprehensive monitoring and optimization of the production process by integrating advanced automation equipment, sensor technology, data analysis, and artificial intelligence. This system can collect production data in real time, automatically adjust production parameters, predict equipment failures and perform preventive maintenance, and at the same time improve production efficiency and flexibility through intelligent scheduling. Intelligent integrated management not only ensures product quality and safety, but also reduces costs, enhances production transparency, and makes dairy beverage production more efficient, precise, and in line with consumers' health needs.

[0004] The solvent leakage detection method plays an important role in the intelligent management system of a dairy beverage production line. This method injects a specific solvent (such as dye or gas) into the sealing system of the equipment and combines intelligent sensors and visual detection technology to monitor the sealing state of the equipment in real time. These sensors can accurately detect the solvent leakage situation and timely identify potential failures such as poor sealing, corrosion, and cracks of the equipment through data analysis. The intelligent detection system can automatically adjust parameters such as the concentration and pressure of solvent injection to ensure the sensitivity of the detection results without affecting the normal operation of the equipment. Through intelligent solvent leakage detection, the production line can achieve more accurate and efficient fault warning and maintenance, ensure the safe and stable operation of the dairy beverage production line, and improve production efficiency and product quality.

[0005] In the above-disclosed technical solutions, there are at least the following technical problems: In the intelligent solvent leakage detection of the production line, the control of the injection pressure is crucial because it directly affects the flow effect of the solvent in the equipment.

[0006] Too high injection pressure may cause the solvent to flow too fast and even break through the seals of the equipment, affecting the accuracy of the detection results and possibly causing damage to the equipment;

[0007] If the injection pressure is too low, the solvent may not effectively penetrate all potential leakage points, reducing the detection sensitivity and missing minor fault signs.

[0008] Due to the differences in the structures and sealing characteristics of different dairy equipment, the optimal injection pressure for each piece of equipment is also different. In the prior art, there are often problems of inaccurate injection pressure control in the intelligent solvent leakage detection of dairy beverage production lines, resulting in insufficient intelligent control sensitivity of production lines of different equipment or equipment damage.

[0009] In view of the above problems, the present invention proposes a solution. Summary of the Invention

[0010] To overcome the above defects of the prior art, an embodiment of the present invention provides an intelligent integrated management system for a dairy beverage production line. By analyzing the injection pressure in the intelligent solvent leakage detection of the production line, it solves the problems in the prior art that there are often inaccurate injection pressure controls in the intelligent solvent leakage detection of dairy beverage production lines, resulting in insufficient intelligent control sensitivity of production lines of different equipment or equipment damage.

[0011] To achieve the above object, the present invention provides the following technical solutions:

[0012] The intelligent integrated management system for a dairy beverage production line includes the following steps: obtaining the operating environment and component data of the dairy beverage production line to be detected, and constructing a geometric model of dairy equipment based on 3D modeling software; endowing the geometric model of dairy equipment and the solvent to be injected with physical properties based on the material library corresponding to the 3D modeling software; obtaining the initial injection pressure for solvent leakage detection based on historical data, and performing random perturbation through a computer to obtain several different injection pressures to be screened; respectively applying different injection pressures to be screened to the geometric model of dairy equipment for solvent leakage detection, and obtaining the leakage path data and solvent and equipment loss data during solvent leakage detection; constructing an injection pressure evaluation model based on the leakage path data and equipment and solvent loss data using a convolutional neural network; constructing a pressure-detection effect display model according to the output of the injection pressure evaluation model and the corresponding injection pressures to be screened, and performing data analysis to obtain the screened injection pressure, which is applied to the injection pressure control of intelligent solvent leakage detection of the dairy beverage production line.

[0013] In a preferred embodiment, the operating environment includes temperature, humidity, pressure conditions, ambient air flow, as well as vibration and noise; the component data includes equipment structure parameters, seal information, material properties, joint and weld characteristics, and fluid channel design; specifically, constructing the geometric model of the dairy equipment based on 3D modeling software is as follows: creating a basic geometric shape using 3D modeling software according to the operating environment and component data; adding detailed structures to the basic geometric shape, and the detailed structures include seams and welds, seals, and flanges and bolts; dividing the geometric model of the dairy equipment into small unit meshes, and refining the distribution of small unit meshes in high-stress areas.

[0014] In a preferred embodiment, obtaining the initial injection pressure for solvent leakage detection based on historical data and performing random perturbations through a computer to obtain several different injection pressures to be screened, specifically: extracting relevant data on solvent leakage detection from the historical detection records of the dairy beverage production line, including the injection pressure range, detection effect, and fault characteristics when leakage occurs; combining the equipment type, operating environment, and solvent characteristics, statistically calculating the commonly used injection pressure values as the initial injection pressure, and taking its average value as the initial injection pressure; performing random perturbations on the initial injection pressure value based on the Monte Carlo method to generate a set of candidate pressure values; screening the generated candidate pressure values according to the equipment and solvent characteristics, and eliminating extreme values that may cause equipment damage or detection failure to obtain several different injection pressures to be screened.

[0015] In a preferred embodiment, the leakage path data includes a detection area coverage influence coefficient and a leakage path influence coefficient; the specific method for obtaining the detection area coverage influence coefficient is as follows: performing unitization processing on the three-dimensional geometric model of the dairy beverage production line, dividing the equipment into multiple small volume units, numbering them to obtain a unit set; counting the number of equipment surface units that affect the solvent diffusion range to obtain the volume of the affected area; identifying and analyzing the solvent diffusion path in the unitized equipment model, and calculating the rate of the diffusion path; analyzing and calculating the detection area coverage influence coefficient by combining the rate of the diffusion path and the volume of the affected area.

[0016] In a preferred embodiment, the specific method for obtaining the leakage path influence coefficient is as follows: based on the solvent leakage detection process under different injection pressures, obtaining a set of paths of the influence of various injection pressures on the detection results, and the set of paths of the influence of various injection pressures on the detection results includes abnormal leakage paths caused by various injection pressures, and performing weight estimation on the abnormal leakage paths based on historical data; constructing a regression model based on the set of paths of the influence of various injection pressures on the detection results and the corresponding weights to generate the leakage path influence coefficient.

[0017] In a preferred embodiment, the solvent and equipment loss data includes a solvent and equipment loss cost coefficient; the specific method for obtaining the solvent and equipment loss cost coefficient is as follows: Obtain the ratios of the solvent consumption to the equipment loss at several different injection pressures; Calculate the standard deviation and average value of the loss costs of the ratios of the solvent consumption to the equipment loss at several different injection pressures; Calculate the coefficient of variation of the ratio of the solvent consumption to the equipment loss based on the standard deviation and average value of the loss costs; Calculate the solvent and equipment loss cost coefficient by calculating the coefficient of variation of the ratio of the solvent consumption to the equipment loss based on a preset solvent and equipment loss cost coefficient calculation formula.

[0018] In a preferred embodiment, the pressure-detection effect display model is constructed based on the output of the injection pressure evaluation model and the corresponding injection pressure to be screened, specifically as follows: Obtain the injection pressure evaluation coefficients output under different injection pressures to be screened from the injection pressure evaluation model; Set the X-axis as the injection pressure to be screened and the Y-axis as the injection pressure evaluation coefficient to establish a rectangular coordinate system; Mark the injection pressure evaluation coefficients corresponding to each injection pressure to be screened on the rectangular coordinate system to form marked points, and connect several marked points with a smooth curve to construct the pressure-detection effect display model.

[0019] In a preferred embodiment, data analysis is performed to obtain the screened injection pressure and apply it to the injection pressure control of intelligent solvent leakage detection in the dairy beverage production line, specifically as follows: Screen the injection pressures to be screened corresponding to the peaks of the pressure-detection effect display model to obtain the first screening group; Compare the second derivatives of the injection pressures to be screened corresponding to each injection pressure in the first screening group on the pressure-detection effect display model, and use the injection pressure with the smallest comparison result as the screened injection pressure and apply it to the injection pressure control of intelligent solvent leakage detection in the dairy beverage production line.

[0020] Technical effects and advantages of the intelligent integrated management system for the dairy beverage production line of the present invention:

[0021] 1. By obtaining the equipment operation environment and component data, establishing a geometric model and assigning corresponding physical properties, the present invention can truly simulate the behavior of the equipment in the case of solvent leakage. Further, by accurately calculating the leakage path and the influence coefficient of the detection area coverage, the injection pressure is optimized to ensure a moderate solvent diffusion range, avoid false detection or missed detection, and at the same time reduce the risk of equipment damage. This method not only adapts to equipment differences, improves the detection consistency among different equipment, but also improves the intelligent control efficiency of the production line by optimizing the solvent flow pattern analysis, solves the problems of insufficient sensitivity and equipment damage existing in the prior art, and thus realizes more accurate and reliable intelligent control in the production line. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 This is a schematic structural diagram of the intelligent integrated management system for the dairy beverage production line of the present invention. Specific embodiments

[0023] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0024] Embodiment 1 Figure 1 An intelligent integrated management system for the dairy beverage production line of the present invention is given, including the following steps:

[0025] S1. Obtain the operating environment and component data of the dairy beverage production line to be detected, and construct a geometric model of dairy equipment based on 3D modeling software.

[0026] The operating environment includes temperature, humidity, pressure conditions, surrounding air flow, and vibration and noise;

[0027] The component data includes equipment structure parameters, seal information, material properties, joint and weld characteristics, and fluid channel design;

[0028] The construction of the geometric model of dairy equipment based on 3D modeling software specifically is:

[0029] Create a basic geometric shape using 3D modeling software according to the operating environment and component data;

[0030] Add detailed structures to the basic geometric shape, and the detailed structures include seams and welds, seals, and flanges and bolts;

[0031] Divide the geometric model of dairy equipment into small unit meshes, and refine the distribution of small unit meshes in high-stress areas.

[0032] It should be noted that refining the mesh distribution in leakage points or high-stress areas means that during the 3D modeling process, more dense mesh division is used for key parts that may leak or bear large stresses to improve the calculation accuracy of these areas, while maintaining a coarser mesh in other secondary areas to reduce the calculation amount. Through this targeted mesh optimization, while ensuring the accuracy of the simulation results, the calculation resources and efficiency can be effectively balanced to meet the analysis requirements of solvent leakage detection.

[0033] It should be noted that seams and welds: Mark the seam positions on the model according to the actual structure, and simulate the welds using line or surface tools. Seals: Create the geometric models of rubber rings or gaskets at the sealed parts of the equipment. Flanges and bolts: Add flanges, bolt holes, etc. at the connection points to enhance the realism of the model.

[0034] It should be noted that common 3D modeling software includes AutoCAD, SolidWorks, ANSYS SpaceClaim, Fusion 360, etc. Select the appropriate modeling software according to the actual equipment characteristics and complexity, which will not be elaborated here.

[0035] It should be noted that the operating environment data refers to the information related to the working state of the equipment and the surrounding injection pressure, which is used to simulate the actual operating conditions of the equipment to ensure the accuracy of 3D modeling and intelligent control of the production line. It mainly includes: Temperature: Affects the physical properties of the equipment materials (such as the coefficient of thermal expansion) and the fluid characteristics of the solvent (such as viscosity and diffusion rate). Humidity: High humidity may affect the solvent diffusion path and interfere with the signal intensity of the detection results. Pressure conditions: The equipment may exhibit different sealing performances under different operating pressures, directly affecting the degree of leakage and the difficulty of detection. Surrounding air flow: External air flow will change the solvent diffusion path and affect the detectability of the leakage signal. Vibration and noise: A high-vibration environment may cause additional stress on the sealing components and interfere with the signal processing of the sensors of the detection equipment.

[0036] The component data involves the structural information and material properties of the dairy beverage production line, which is the basis for constructing the 3D geometric model and endowing physical properties. It mainly includes: Equipment structure parameters: including the external dimensions of the equipment, the internal pipeline layout, the seam positions, etc., which determine the distribution of possible leakage points and the solvent diffusion path. Seal information: including the material, thickness, aging degree, etc. of the seals, which affect the sealing performance and are the key components for leakage detection. Material characteristics: The physical properties such as the elastic modulus, thermal conductivity, and corrosion resistance of each component are used to simulate the real leakage conditions. Joint and weld characteristics: including the joint type, weld position and width, etc., which are high-risk areas where solvents may leak. Fluid channel design: involves the flow paths of internal liquids and gases, which has an important impact on simulating solvent diffusion and leakage behavior.

[0037] S2. Endow the physical properties to the geometric model of the dairy equipment and the solvent to be injected based on the material library corresponding to the 3D modeling software.

[0038] The endowing of the physical properties to the geometric model of the dairy equipment and the solvent to be injected based on the material library corresponding to the 3D modeling software is specifically as follows:

[0039] Endow the geometric model and the solvent to be injected with physical properties based on the material library to simulate their real characteristics.

[0040] It should be noted that based on the material library corresponding to the 3D modeling software, by selecting or customizing the physical properties of the equipment and the solvent, material properties (such as density, elastic modulus, thermal conductivity, etc.) are assigned to the geometric model of the dairy equipment, and fluid properties (such as density, viscosity, flow pressure, etc.) are assigned to the solvent to be injected. At the same time, for different components of the equipment and their contact surfaces with the solvent, the distribution of material properties and the definition of contact conditions are refined to ensure that the model can truly reflect the mechanical and fluid behaviors of the equipment in the intelligent solvent leakage detection on the production line, providing basic support for accurate fault simulation and analysis.

[0041] S3. Obtain the initial injection pressure for solvent leakage detection based on historical data, and perform random perturbations through a computer to obtain several different injection pressures to be screened.

[0042] The process of obtaining the initial injection pressure for solvent leakage detection based on historical data and performing random perturbations through a computer to obtain several different injection pressures to be screened is specifically as follows:

[0043] Extract relevant data for solvent leakage detection from the historical detection records of the dairy beverage production line, including the injection pressure range, detection effect, and fault characteristics when leakage occurs;

[0044] Combine the equipment type, operating environment, and solvent characteristics, and statistically calculate the commonly used injection pressure values as the initial injection pressure, and use its average value as the initial injection pressure;

[0045] Perform random perturbations on the initial injection pressure value based on the Monte Carlo method to generate a set of candidate pressure values;

[0046] According to the equipment and solvent characteristics, screen the generated candidate pressure values, and eliminate extreme values that may cause equipment damage or detection failure to obtain several different injection pressures to be screened.

[0047] It should be noted that the process of screening the generated candidate pressure values according to the equipment and solvent characteristics belongs to the existing mature technology, which usually includes comprehensive evaluation through material strength analysis, equipment pressure resistance test results, and solvent fluid mechanics characteristics to eliminate extreme pressure values that may cause equipment damage or ineffective solvent leakage. For example, it can be judged whether certain pressure values exceed the equipment safety range or cause excessive detection errors through simulation calculation or experimental verification. Since the relevant methods have been widely applied in the industrial detection field, their principles are clear and the technology is mature, so they will not be elaborated here.

[0048] S4. Apply different injection pressures to be screened to the geometric model of the dairy equipment for solvent leakage detection, and obtain the leakage path data and solvent and equipment loss data during solvent leakage detection.

[0049] The leakage path data includes a detection area coverage influence coefficient and a leakage path influence coefficient;

[0050] The solvent and equipment loss data includes a solvent and equipment loss cost coefficient.

[0051] The detection area coverage influence coefficient is used to evaluate the effectiveness of solvent diffusion during the solvent leakage detection process. It reflects the influence of the injection pressure on the solvent diffusion range. Excessive pressure may cause the solvent to diffuse excessively, covering unnecessary detection areas and increasing the risk of false detection; while too low pressure may result in insufficient solvent diffusion, failing to cover all potential leakage points, thus affecting the comprehensiveness and accuracy of the detection. Through this coefficient, the coverage of the detection area can be effectively measured, ensuring the sensitivity and precision of the intelligent control of the production line, and avoiding problems such as insufficient detection effect or equipment damage caused by improper injection pressure.

[0052] Analyzing the detection area coverage influence coefficient has the following advantages for solving the problems in the prior art that in the intelligent solvent leakage detection of dairy beverage production lines, the injection pressure control is often inaccurate, resulting in insufficient sensitivity of the intelligent control of production lines of different equipment or equipment damage:

[0053] Improve detection accuracy: By reasonably evaluating and adjusting the injection pressure, the detection area coverage influence coefficient can ensure an appropriate solvent diffusion range, avoiding false detection or missed detection caused by too high or too low pressure, thus improving the accuracy of the intelligent control of the production line.

[0054] Optimize detection sensitivity: This coefficient helps to precisely adjust the injection pressure according to the equipment type and operating environment, optimizing the sensitivity of the intelligent control of production lines of different equipment, avoiding insufficient sensitivity caused by inaccurate pressure, and ensuring that equipment failures can be detected in a timely manner.

[0055] Reduce the risk of equipment damage: By precisely controlling the injection pressure, the detection area coverage influence coefficient can avoid potential damage to equipment caused by excessive pressure, especially on high-pressure components, reducing the risk of equipment damage caused by excessive diffusion.

[0056] Adapt to equipment differences: This coefficient can flexibly adjust the pressure value according to the characteristics of different dairy equipment, ensuring the best detection effect for each equipment, and avoiding inconsistent detection caused by mismatched injection pressures between equipment.

[0057] Improve the efficiency of intelligent control of the production line: Through refined pressure control, the detection area coverage influence coefficient can ensure that all potential leakage points are covered, reducing the situation of missed detection, and improving the efficiency and timeliness of the intelligent control of the production line.

[0058] In summary, by optimizing the control of the injection pressure, the detection area coverage influence coefficient effectively solves the problems of low accuracy, insufficient sensitivity, and equipment damage in the prior art, making the intelligent control of the dairy beverage production line more accurate and reliable.

[0059] The specific method for obtaining the detection area coverage influence coefficient is as follows:

[0060] Perform unitization on the three-dimensional geometric model of the dairy beverage production line, divide the equipment into multiple small volume units, label them, and obtain the unit set;

[0061] Count the number of equipment surface units that affect the solvent diffusion range to obtain the volume of the affected area;

[0062] Identify and analyze the solvent diffusion path in the unitized equipment model, and calculate the rate of the diffusion path; combine the rate of the diffusion path and the volume of the affected area to analyze and calculate the detection area coverage influence coefficient;

[0063] The calculation formula for the volume of the affected area is:

[0064]

[0065] The calculation formula for the rate of the diffusion path is:

[0066]

[0067] The calculation formula for the detection area coverage influence coefficient is:

[0068]

[0069] In the formula, α is the detection area coverage influence coefficient, N is the total number of units in the unit set, i is the unit label; V i is the volume of the i-th unit, and Dld i is the leakage path resistance length of the i-th diffusion path.

[0070] The leakage path influence coefficient is used to evaluate the influence of the injection pressure on the flow pattern of the solvent in the leakage path, thereby affecting the judgment accuracy of the leakage point position. Different injection pressures will change the flow characteristics of the solvent. For example, high pressure may cause turbulence, while low pressure may result in laminar flow. These changes in the flow pattern will affect the propagation of the solvent in the leakage path, thereby affecting the detection accuracy. By analyzing the flow pattern of the leakage path, the leakage path influence coefficient can help optimize the leakage point positioning algorithm, improve the sensitivity and accuracy of the intelligent control of the production line, and effectively solve the problems of insufficient detection sensitivity and equipment damage caused by inaccurate injection pressure control in the prior art.

[0071] Analyzing the leakage path influence coefficient has the following advantages for solving the problems in the prior art that in the intelligent solvent leakage detection of dairy beverage production lines, the injection pressure control is often inaccurate, resulting in insufficient sensitivity of intelligent control of production lines of different equipment or equipment damage:

[0072] Optimize the flow pattern judgment: By evaluating the flow pattern in the leakage path, the leakage path influence coefficient can accurately reflect the flow characteristics of the solvent (such as turbulent flow and laminar flow) under different injection pressures, thereby improving the accuracy of leakage point location judgment and avoiding missed detection or false detection problems caused by changes in the flow pattern.

[0073] Improve the sensitivity of intelligent control of the production line: This coefficient can help analyze the influence of the flow pattern in the leakage path on the detection sensitivity, so as to optimize the injection pressure according to the equipment type and operating environment, improving the detection sensitivity and avoiding insufficient sensitivity of intelligent control of the production line caused by inaccurate injection pressure.

[0074] Reduce the risk of equipment damage: By optimizing the flow pattern of the leakage path, the leakage path influence coefficient can ensure the stability of the solvent flow in the leakage path, avoiding high-pressure-induced turbulence or insufficient flow caused by too low pressure, thereby reducing the risk of equipment damage and ensuring equipment safety.

[0075] Adapt to different equipment differences: This coefficient can adjust the solvent flow pattern according to the characteristics of different dairy equipment, ensuring that the intelligent control effect of the production line of the equipment matches the design and function of the equipment itself, and avoiding problems such as mismatched injection pressure and inconsistent detection effects caused by equipment differences.

[0076] Improve the accuracy of fault location: The leakage path influence coefficient can optimize the fault location algorithm. Based on the flow pattern and leakage path data, it improves the accuracy of fault location, ensures that potential leakage points are accurately identified, and thus improves the reliability and accuracy of the detection process.

[0077] In summary, by optimizing the flow characteristics of the solvent in the leakage path, the leakage path influence coefficient can effectively solve the problems of insufficient sensitivity of intelligent control of the production line, equipment damage, and inaccurate detection caused by inaccurate injection pressure control in the prior art, thereby realizing the high efficiency, accuracy, and safety of intelligent control of dairy beverage production lines.

[0078] The specific method for obtaining the leakage path influence coefficient is as follows:

[0079] Based on the solvent leakage detection process under different injection pressures, obtain a set of paths of the influence of various injection pressures on the detection results. The set of paths of the influence of various injection pressures on the detection results includes abnormal leakage paths caused by various injection pressures, and perform weight estimation on the abnormal leakage paths based on historical data;

[0080] Construct a regression model based on the path set and corresponding weights of the influence of various injection pressures on the detection results, and generate the leakage path influence coefficient.

[0081] The specific calculation formula of the leakage path influence coefficient is as follows:

[0082]

[0083] In the formula, β is the leakage path influence coefficient, μ1……μ n is the weight estimation value corresponding to the path in the solvent leakage detection process under different injection pressures, ω1……ω n is the proportion of the path in the solvent leakage detection process under different injection pressures in all the paths of the solvent leakage detection process, YC n is the corresponding detection accuracy of the path in the solvent leakage detection process under different injection pressures, n is the number of the path in the solvent leakage detection process under different injection pressures, and e is a natural number.

[0084] The solvent and equipment loss cost coefficient is used to evaluate the consumption of the solvent in the solvent leakage detection process and its impact on the equipment. High injection pressure will lead to an increase in solvent consumption, which may cause unnecessary waste; while low injection pressure may not be able to inject the solvent sufficiently, affecting the continuity of the detection effect. This coefficient also pays attention to equipment loss. Excessive injection pressure may cause an additional burden on the equipment and even lead to secondary damage to the sealing components; low pressure may result in insufficient detection effect and delay the discovery of problems. By evaluating the solvent consumption and equipment status data, the solvent and equipment loss cost coefficient can help judge the economy, safety and sustainability of the intelligent control of the production line, and avoid the problems of insufficient detection sensitivity and equipment damage caused by inaccurate injection pressure control in the prior art.

[0085] Analyzing the solvent and equipment loss cost coefficient has the following advantages for solving the problems in the prior art that there are often inaccurate injection pressure controls in the intelligent solvent leakage detection of the dairy beverage production line, resulting in insufficient sensitivity of the intelligent control of different equipment production lines or equipment damage:

[0086] The specific acquisition method of the solvent and equipment loss cost coefficient is as follows:

[0087] Obtain the ratio of the solvent consumption to the equipment loss amount at several different injection pressures;

[0088] Calculate the loss cost standard deviation and loss cost average value of the ratio of the solvent consumption to the equipment loss amount at several different injection pressures;

[0089] Calculate the coefficient of variation of the ratio of the solvent consumption to the equipment loss amount according to the loss cost standard deviation and loss cost average value;

[0090] Calculate the cost coefficient of solvent and equipment loss based on the coefficient calculation formula of solvent consumption and equipment loss with the coefficient of variation of the ratio of solvent consumption to equipment loss.

[0091] The specific calculation formula of the coefficient of variation of the ratio of the solvent consumption to the equipment loss is as follows:

[0092]

[0093] The specific calculation formula of the cost coefficient of solvent and equipment loss is as follows:

[0094]

[0095] In the formula, μ is the coefficient of variation of the ratio of solvent consumption to equipment loss, wl j is the ratio of the jth solvent consumption to equipment loss, N is the total number of collected data, j is the data label; χ is the cost coefficient of solvent and equipment loss.

[0096] In this embodiment, by obtaining the equipment operation environment and component data, establishing a geometric model and endowing corresponding physical properties, the behavior of the equipment under solvent leakage conditions can be truly simulated. Further, by accurately calculating the leakage path and the influence coefficient of the detection area coverage, the injection pressure is optimized to ensure a moderate solvent diffusion range, avoid false detection or missed detection, and at the same time reduce the risk of equipment damage. This method not only adapts to equipment differences, improves the detection consistency among different equipment, but also improves the intelligent control efficiency of the production line by optimizing the analysis of the solvent flow pattern, solves the problems of insufficient sensitivity and equipment damage existing in the prior art, and thus realizes more accurate and reliable intelligent control in the production line.

[0097] Embodiment 2, S5, construct an injection pressure evaluation model based on the convolutional neural network according to the leakage path data and the equipment and solvent loss data.

[0098] The construction of the injection pressure evaluation model based on the convolutional neural network according to the defect location data and the detection robustness data is specifically as follows:

[0099] Establish an injection pressure evaluation model with the obtained influence coefficient of the detection area coverage, the influence coefficient of the leakage path, and the cost coefficient of solvent and equipment loss, and generate an injection pressure evaluation coefficient;

[0100] The specific calculation formula of the injection pressure evaluation coefficient is as follows:

[0101]

[0102] Wherein, α is the detection area coverage influence coefficient, β is the leakage path influence coefficient, χ is the solvent and equipment loss cost coefficient, YL is the injection pressure evaluation coefficient, λ1 is the preset proportional coefficient of the detection area coverage influence coefficient, λ2 is the preset proportional coefficient of the leakage path influence coefficient, and λ3 is the preset proportional coefficient of the solvent and equipment loss cost coefficient.

[0103] S6. According to the output of the injection pressure evaluation model and the corresponding injection pressure to be screened, construct a pressure-detection effect display model, and conduct data analysis to obtain the screened injection pressure, which is applied to the injection pressure control of intelligent solvent leakage detection in the dairy beverage production line.

[0104] The construction of the pressure-detection effect display model according to the output of the injection pressure evaluation model and the corresponding injection pressure to be screened is specifically as follows:

[0105] Obtain the injection pressure evaluation coefficients output under different injection pressures to be screened from the injection pressure evaluation model;

[0106] Set the X-axis as the injection pressure to be screened and the Y-axis as the injection pressure evaluation coefficient to establish a rectangular coordinate system;

[0107] Mark the injection pressure evaluation coefficients corresponding to each injection pressure to be screened on the rectangular coordinate system to form marked points, and connect several marked points with a smooth curve to construct a pressure-detection effect display model.

[0108] The data analysis and obtaining the screened injection pressure, which is applied to the injection pressure control of intelligent solvent leakage detection in the dairy beverage production line, is specifically as follows:

[0109] Screen the injection pressures to be screened corresponding to the peaks of the pressure-detection effect display model to obtain the first screening group;

[0110] Compare the second derivatives of the injection pressures to be screened corresponding to each injection pressure to be screened in the first screening group on the pressure-detection effect display model, and use the injection pressure with the smallest comparison result as the screened injection pressure, which is applied to the injection pressure control of intelligent solvent leakage detection in the dairy beverage production line.

[0111] The above formulas are all dimensionless and take their numerical values for calculation. The formula is obtained by collecting a large amount of data for software simulation to obtain a formula closest to the actual situation. The preset parameters in the formula are set by those skilled in the art according to the actual situation.

[0112] The above embodiments can be implemented in whole or in part by 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.

[0113] Those of ordinary skill in the art will appreciate that the modules and algorithm steps of each example described in connection with the embodiments disclosed herein can be implemented in electronic hardware, or in a combination of computer software and electronic hardware. Whether these functions are executed in hardware or software depends on the specific application and design constraints of the technical solution. Skilled professionals can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of this application.

[0114] In addition, in each of the embodiments of this application, the functional modules can be integrated into one processing module, or each module can exist physically alone, or two or more modules can be integrated into one module.

[0115] As described above, this is only the specific implementation manner of this application, but the protection scope of this application is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed in this application, and all such changes or substitutions should be covered by the protection scope of this application. Therefore, the protection scope of this application should be subject to the protection scope of the claims.

[0116] Finally: The above is only the preferred embodiment of the present invention and is not used to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. Intelligent integrated management system for dairy beverage production line, characterized by: It includes modeling module, refinement module, injection pressure acquisition module, data acquisition module, injection pressure evaluation module and injection pressure screening module: Modeling module, used to obtain the operating environment and component data of the dairy beverage production line to be tested, and build a geometric model of the dairy equipment based on 3D modeling software; A refinement module is used to assign physical properties to the geometric model of the dairy equipment and the solvent to be injected based on the material library corresponding to the 3D modeling software; An injection pressure acquisition module is used to obtain the initial injection pressure for solvent leakage detection based on historical data, and to perform random perturbations through a computer to obtain several different injection pressures to be screened; A data acquisition module, used for respectively applying different injection pressures to be screened to perform solvent leakage detection on the geometric model of dairy equipment, and obtaining leakage path data and solvent and equipment loss data during solvent leakage detection; An injection pressure assessment module, which is used to build an injection pressure assessment model based on a convolutional neural network according to the leakage path data and the equipment and solvent loss data; An injection pressure screening module is used to construct a pressure-detection effect display model according to the output of the injection pressure evaluation model and the corresponding injection pressure to be screened, and screen the injection pressure to be screened corresponding to each peak value of the pressure-detection effect display model to obtain a first screening group; The quadratic derivatives of each injection pressure to be screened in the first screening group on the pressure-detection effect display model are compared, and the injection pressure to be screened with the smallest comparison result is used as the injection pressure after screening, and is applied to the injection pressure control of intelligent solvent leakage detection in the dairy beverage production line.

2. The intelligent integrated management system for the dairy beverage production line according to claim 1, characterized in that: The operating environment includes temperature, humidity, pressure conditions, ambient air flow, vibration and noise; The component data includes equipment structural parameters, seal information, material properties, joint and weld properties, and fluid channel design; The geometric model of dairy equipment constructed based on 3D modeling software is specifically as follows: Use 3D modeling software to create basic geometric shapes based on the operating environment and component data; adding detailed structures to the basic geometry, including seams and welds, seals, and flanges and bolts; The geometric model of dairy equipment is divided into small unit grids, and the distribution of small unit grids in high stress areas is refined.

3. The intelligent integrated management system for the dairy beverage production line according to claim 2, characterized in that: The initial injection pressure for solvent leakage detection is obtained based on historical data, and random perturbations are performed by a computer to obtain several different injection pressures to be screened, specifically: Extract relevant data of solvent leakage detection from the historical detection records of dairy beverage production lines, including the injection pressure range, detection effect and fault characteristics when leakage occurs; Combined with the equipment type, operating environment and solvent characteristics, the commonly used injection pressure values ​​are counted and calculated as the initial injection pressure, and the average value is used as the initial injection pressure; Perform random perturbations on the initial injection pressure value based on the Monte Carlo method to generate a set of candidate pressure values; According to the characteristics of the equipment and solvent, the generated candidate pressure values ​​are screened, and extreme values ​​that may cause equipment damage or detection failure are eliminated, thereby obtaining several different injection pressures to be screened.

4. The intelligent integrated management system for the dairy beverage production line according to claim 3, characterized in that: The leakage path data includes the detection area coverage influence coefficient and the leakage path influence coefficient; the specific method for obtaining the detection area coverage influence coefficient is as follows: Unitize the three-dimensional geometric model of the dairy beverage production line, divide the equipment into multiple small volume units, label them and obtain a unit set; Count the number of equipment surface units that affect the solvent diffusion range and obtain the volume of the affected area; The solvent diffusion path is identified and analyzed in the unitized equipment model, and the rate of the diffusion path is calculated; the detection area coverage influence coefficient is calculated by combining the rate of the diffusion path and the volume analysis of the affected area.

5. The intelligent integrated management system for the dairy beverage production line according to claim 4, characterized in that: The specific method for obtaining the leakage path influence coefficient is as follows: Based on the solvent leakage detection process under different injection pressures, a set of paths of influence of various injection pressures on the detection results is obtained, wherein the set of paths of influence of various injection pressures on the detection results includes abnormal leakage paths caused by various injection pressures, and a weighted valuation is performed on the abnormal leakage paths based on historical data; A regression model is constructed based on the path set and corresponding weights of the impact of various injection pressures on the detection results to generate the leakage path influence coefficient.

6. The intelligent integrated management system for the dairy beverage production line according to claim 5, characterized in that: The solvent and equipment loss data include solvent and equipment loss cost coefficients; the specific method for obtaining the solvent and equipment loss cost coefficients is as follows: Obtain the ratio of solvent consumption to equipment loss at several different injection pressures; Calculate the loss cost standard deviation and the loss cost average of the ratio of solvent consumption to equipment loss at different injection pressures; According to the standard deviation of loss cost and the average value of loss cost, calculate the coefficient of variation of the ratio of solvent consumption to equipment loss; The solvent and equipment loss cost coefficients are calculated based on the preset solvent and equipment loss cost coefficient calculation formula using the coefficient of variation of the ratio of solvent consumption to equipment loss.

7. The intelligent integrated management system for the dairy beverage production line according to claim 6, characterized in that: According to the output of the injection pressure evaluation model and the corresponding injection pressure to be screened, a pressure-detection effect display model is constructed, specifically: Obtaining injection pressure assessment coefficients of outputs under different injection pressures to be screened from the injection pressure assessment model; The X-axis is set as the injection pressure to be screened, and the Y-axis is represented as the injection pressure evaluation coefficient, and a rectangular coordinate system is established; The injection pressure assessment coefficient corresponding to each injection pressure to be screened is marked on the rectangular coordinate system to form marking points, and a smooth curve is used to smoothly connect several marking points to construct a pressure-detection effect display model.

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