Noodle conveying device and process of noodle cooking machine

Abstract

The present application relates to the technical field of food processing, and discloses a noodle conveying device and process for a noodle cooking machine. The conveying device comprises a mounting frame, a rotating shaft, a chain and a guide groove. The chain is slidingly embedded in the guide groove, and adjacent chains are fixed with a metal mesh through a connecting rod, effectively constraining the offset trajectory of the chain in a high-temperature and high-humidity environment, and improving the conveying stability. The conveying process comprises: obtaining the initial moisture content and cross-sectional equivalent diameter of the noodles, and calculating the raw material demand coefficient; collecting the steam temperature, saturation and flow rate, and calculating the heat source supply coefficient; determining the mesh belt load and noodle arrangement density, and calculating the heat transfer efficiency coefficient in combination with the raw material demand coefficient and the heat source supply coefficient; detecting the gelatinization degree and color difference value, and calculating the quality feedback coefficient; and calculating the target conveying belt running speed and adjusting it according to the reference speed, the heat transfer efficiency coefficient and the quality feedback coefficient. The dynamic adaptive adjustment of the conveying belt speed is realized, and the noodle cooking uniformity and product quality stability are significantly improved.

Description

Technical Field

[0001] This invention belongs to the field of food processing technology, and in particular relates to a noodle conveying device and process for a noodle cooking machine. Background Technology

[0002] In the industrial production of noodles, the cooking process relies on continuous conveyor equipment and a steam cooking zone to gelatinize and shape the noodles. Existing conveyor systems generally use chain-driven metal mesh belts. The chain, driven by sprockets, is prone to uneven tension and misalignment under prolonged high temperature and humidity, leading to belt deviation, noodle slippage, or localized accumulation, severely compromising cooking uniformity. While some equipment incorporates simple guide rails, the guiding structure lacks stability under harsh conditions, failing to effectively constrain belt movement. Furthermore, the mesh belt is complex to disassemble and maintain, and cannot flexibly adapt to the load requirements of different noodle sizes. At the process control level, current equipment often uses preset fixed speeds or relies on manual experience for adjustment, resulting in overly rudimentary control strategies. In actual production, the characteristics of noodle raw materials fluctuate significantly. Differences in the initial moisture content of different batches of noodles directly affect the moisture migration rate, while changes in cross-sectional dimensions alter the heat conduction path length. These factors combined cause dynamic variations in cooking difficulty. Simultaneously, heat source parameters such as steam temperature, saturation, and flow rate in the cooking zone fluctuate continuously due to boiler load and ambient humidity, resulting in unstable heating capacity. If the noodle packing density and load on the conveyor belt exceed a reasonable range, it will hinder steam penetration through the mesh, significantly reducing thermal efficiency and causing quality problems such as localized undercooking or uneven coloring. Existing control methods can only detect deviations through post-event sampling, lacking the ability to perceive and collaboratively analyze multi-source disturbances such as raw material characteristics, heat source status, and conveyor belt load in real time. This results in a serious lag in speed adjustment, making it impossible to achieve an optimal balance between production capacity and energy consumption while ensuring uniform gelatinization and color consistency. Summary of the Invention

[0003] The purpose of this invention is to provide a noodle conveying device and process for a noodle cooking machine, in order to solve the above-mentioned problems.

[0004] The present invention is implemented as follows: a noodle conveying device for a noodle cooking machine includes a mounting frame and a rotating shaft rotatably connected to both ends of the mounting frame. Two chains are connected to the two rotating shafts via sprocket transmission. Guide grooves are provided on both inner walls of the mounting frame. The two chains are slidably connected in the two guide grooves respectively. The guide grooves are used to limit the movement trajectory of the chains. Adjacent chains are fixedly connected by multiple connecting rods. A metal mesh is fixed to the outside of the multiple connecting rods.

[0005] A noodle conveying process for a noodle cooking machine includes the following steps: obtaining the initial moisture content and equivalent cross-sectional diameter of the noodles, and calculating the raw material demand coefficient characterizing the difficulty of noodle cooking; collecting the steam temperature, steam saturation, and steam flow rate of the cooking zone, and calculating the heat source supply coefficient characterizing the heating capacity; determining the current mesh belt load and noodle arrangement density, and calculating the heat transfer efficiency coefficient characterizing the steam thermal energy utilization efficiency based on the current raw material demand coefficient and heat source supply coefficient; detecting the gelatinization degree and color difference value of the noodles, and calculating the quality feedback coefficient characterizing the actual cooking quality; calling a preset reference speed, and calculating the target conveyor belt running speed based on the reference speed, the heat transfer efficiency coefficient, and the quality feedback coefficient, and adjusting the current conveyor belt running speed to the target conveyor belt running speed.

[0006] A further technical solution involves calculating the raw material demand coefficient as follows: First, obtain the initial moisture content of the noodles and the equivalent diameter of the noodle cross-section. Second, ratio the difference between the current initial moisture content and the minimum initial moisture content with the difference between the maximum and minimum initial moisture content to obtain the initial moisture content index. Third, ratio the difference between the square of the equivalent diameter of the noodle cross-section and the square of the minimum cross-section diameter with the difference between the square of the maximum and the square of the minimum cross-section diameter to obtain the equivalent diameter index. Finally, substitute the initial moisture content index and the equivalent diameter index into the formula... Obtain the raw material demand coefficient ,in, The initial moisture content index. The equivalent diameter index of the cross section.

[0007] A further technical solution involves the following calculation process for the heat source supply coefficient: obtaining the steam temperature, steam saturation, and steam velocity of the maturation zone; performing maximum and minimum normalization on the steam temperature, steam saturation, and steam velocity of the maturation zone to obtain the steam temperature index, steam saturation index, and steam velocity index; geometrically averaging the steam temperature index and the steam saturation index to obtain the steam heat intensity factor; and geometrically averaging the steam heat intensity factor and the steam velocity index to obtain the heat source supply coefficient.

[0008] A further technical solution involves normalizing the steam temperature, steam saturation, and steam flow rate in the maturation zone as follows: The difference between the current steam temperature and the lowest steam temperature is compared with the difference between the highest steam temperature and the lowest steam temperature to obtain a steam temperature index; the difference between the current steam saturation and the lowest saturation is compared with the difference between the highest saturation and the lowest saturation to obtain a steam saturation index; and the difference between the current steam flow rate and the lowest steam flow rate is compared with the difference between the highest steam flow rate and the lowest steam flow rate to obtain a steam flow rate index.

[0009] A further technical solution involves calculating the heat transfer efficiency coefficient as follows: obtaining the raw material demand coefficient, heat source supply coefficient, mesh belt load, and noodle arrangement density; and substituting the raw material demand coefficient and heat source supply coefficient into the formula. Obtain the ripening difficulty factor ,in, This is the raw material demand coefficient. For heat source supply coefficient, The values ​​are minimal positive constants; based on the cooking difficulty factor, conveyor belt load, and noodle arrangement density, the load deviation index and density deviation index are determined; the load deviation index and density deviation index are substituted into the formula. Obtain the heat transfer efficiency coefficient ,in, The load deviation index. This is the density deviation index.

[0010] A further technical solution involves calculating the load deviation index and density deviation index as follows: Based on the ripening difficulty factor, the optimal load under the current working condition is determined by linear interpolation within the allowable load range of the conveyor belt, and the optimal noodle arrangement density under the current working condition is determined by linear interpolation within the allowable noodle arrangement density range; wherein, the larger the ripening difficulty factor, the closer the optimal load and the optimal arrangement density are to the lower limit of their allowable range; the difference between the current conveyor belt load and the optimal load under the current working condition is compared with the difference between the maximum allowable load and the minimum allowable load of the conveyor belt to obtain the load deviation index; the difference between the current noodle arrangement density and the optimal arrangement density under the current working condition is compared with the difference between the maximum noodle arrangement density and the minimum noodle arrangement density to obtain the density deviation index.

[0011] A further technical solution involves calculating the quality feedback coefficient as follows: obtaining the degree of gelatinization and color difference value; and substituting the current degree of gelatinization into the formula. Obtain the gelatinization index ,in, The current degree of gelatinization, For the target degree of gelatinization, Standard deviation, Used to control tolerance; the current color difference value is compared with the maximum permissible color difference, and after limiting the ratio to 1, the color difference index is obtained; the gelatinization index is... Substitute the color difference index into the formula Obtain the quality feedback coefficient ,in, The degree of gelatinization index. This is the color difference index.

[0012] A further technical solution involves calculating the target conveyor belt speed as follows: obtaining a reference speed, a heat transfer efficiency coefficient, and a mass feedback coefficient; multiplying the reference speed with the heat transfer efficiency coefficient and the mass feedback coefficient to obtain the target conveyor belt speed; wherein the heat transfer efficiency coefficient and the mass feedback coefficient together constitute a collaborative correction factor for the reference speed.

[0013] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0014] 1. This invention effectively constrains the running trajectory of the chain in high temperature and high humidity environments through the sliding interlocking structure of the guide groove and the chain, avoiding the problems of belt deviation and noodle slippage caused by uneven chain tension or deviation, and significantly improving the transmission stability and maintenance convenience.

[0015] 2. This invention achieves real-time dynamic adaptive adjustment of the conveyor belt speed by constructing a multi-dimensional evaluation system including raw material demand coefficient, heat source supply coefficient, heat transfer efficiency coefficient, and quality feedback coefficient. This system can accurately compensate for the impact of raw material characteristic fluctuations, heat source state changes, and mesh belt load differences on the cooking effect, and significantly improve the uniformity of noodle cooking and the consistency of product quality. Attached Figure Description

[0016] Figure 1 A schematic diagram of the noodle conveying device of a noodle cooking machine provided by the present invention;

[0017] Figure 2 Provided by the present invention Figure 1 A schematic diagram of the structure of the middle chain;

[0018] Figure 3 Provided by the present invention Figure 1 Schematic diagram of the mounting bracket;

[0019] Figure 4 A flowchart of the noodle conveying process of a noodle cooking machine provided by the present invention.

[0020] In the attached diagram: 1. Mounting bracket; 2. Rotating shaft; 3. Chain; 4. Guide groove; 5. Connecting rod. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0022] In traditional industrial noodle production, the conveyor belt in the cooking process is prone to uneven tension and deviation due to the chain drive structure in high temperature and humidity environments. This causes the metal mesh belt to deviate, resulting in noodles slipping or piling up in certain areas. Furthermore, the failure of the guide structure makes it difficult to maintain the stability of the conveyor trajectory. In addition, the control strategy cannot perceive the differences in raw material characteristics and fluctuations in heat source parameters in real time, resulting in a lag in the adjustment of the mesh belt speed. This damages the uniformity of the cooking process and affects the gelatinization and shaping effect as well as the consistency of product color.

[0023] For example, when producing a batch of wide-section noodles with a high initial moisture content in a high-temperature and high-humidity environment, the conveyor chain deviates due to tension changes, the metal mesh belt deviates from the predetermined trajectory, and some noodles slip to the bottom of the equipment. At the same time, the steam temperature in the cooking zone decreases due to boiler load fluctuations, and the steam saturation and flow rate also fluctuate. The mesh belt load exceeds the appropriate range, the noodle density increases, steam penetration is obstructed, resulting in a decrease in heat transfer efficiency in local areas, and the noodles become undercooked or over-gelatinized. The control unit fails to detect changes in the raw material demand coefficient and heat source supply coefficient in time, and the mesh belt speed is not dynamically adjusted, ultimately resulting in substandard product quality.

[0024] If the above problems are not solved, insufficient conveying stability will frequently cause noodles to slip and pile up, increasing the frequency of production interruptions; the lag in the control strategy will cause the maturation process to be unable to adapt to multi-source disturbances, resulting in the degree of gelatinization and color difference values ​​continuously deviating from the standard range, seriously affecting the product qualification rate and the continuous operation capability of the production line, thereby reducing the overall production efficiency and resource utilization.

[0025] The specific implementation of the present invention will be described in detail below with reference to specific embodiments.

[0026] like Figure 1 , Figure 2 and Figure 3 As shown, a noodle conveying device for a noodle cooking machine according to an embodiment of the present invention includes a mounting frame 1, and further includes:

[0027] The mounting frame 1 is rotatably connected to two rotating shafts 2. Two chains 3 are connected to the two rotating shafts 2 via sprocket transmission. Guide grooves 4 are provided on both inner walls of the mounting frame 1. The two chains 3 are slidably connected in the two guide grooves 4 respectively. The guide grooves 4 are used to limit the movement trajectory of the chains 3. Adjacent chains 3 are fixedly connected by multiple connecting rods 5. Metal mesh is fixed on the outside of the multiple connecting rods 5.

[0028] The core innovation of this embodiment lies in the fact that by combining the guide groove 4 with the chain 3 in a sliding engagement manner, the offset tendency of the chain 3 in high temperature and high humidity environments is effectively constrained. This solves the problems of belt deviation and noodle slippage caused by uneven chain tension in the prior art, thereby improving the stability of the transmission. Specifically, the guide groove 4 is set on the inner walls of both sides of the mounting frame 1. Its structural depth and width are adapted to ensure that the chain 3 is always confined within the preset trajectory during movement, avoiding the defects of traditional simple guide rails that are prone to failure in steam environments. Furthermore, adjacent chains 3 are rigidly fixed by connecting rods 5, and the metal mesh is directly fixed to the outside of the connecting rods 5. This structural design not only strengthens the overall transmission rigidity but also simplifies the belt disassembly and assembly process, significantly improving maintenance convenience.

[0029] During operation, a servo motor drives the rotating shaft 2 to rotate. The rotating shaft 2, through a sprocket, drives the chain 3 to move along the inner wall of the guide groove 4. The displacement of the chain 3 is synchronously transmitted to the metal mesh via the connecting rod 5, achieving continuous noodle conveying. Due to the guiding groove 4's role in limiting the trajectory of the chain 3, even under the high humidity and heat conditions of the steam cooking zone, the chain 3 will not experience lateral deviation or slackness, thus ensuring stable operation of the metal mesh and a uniform distribution of noodle density. This solution replaces the traditional, crude control method that relies on manual adjustment with a structured guiding mechanism, fundamentally eliminating the phenomenon of localized noodle accumulation or slippage caused by unstable conveying, and providing a reliable foundation for precise control of the subsequent cooking process.

[0030] Through the above technical solutions, the operational reliability of the noodle conveying device in high temperature and high humidity environments is significantly enhanced, the load adaptability of the mesh belt is optimized, and the efficiency of disassembly and maintenance is improved, effectively overcoming the technical bottlenecks of insufficient conveying stability and inconvenient maintenance in the background technology.

[0031] like Figure 4 The diagram illustrates a noodle conveying process for a noodle cooking machine according to an embodiment of the present invention, comprising the following steps:

[0032] The initial moisture content and equivalent cross-sectional diameter of the noodles were obtained, and the raw material requirement coefficient, which characterizes the difficulty of cooking the noodles, was calculated. The initial moisture content refers to the percentage of water contained in the noodles before entering the cooking zone. This parameter directly affects the noodles' ability to absorb heat and the cooking time required. The equivalent cross-sectional diameter refers to the geometric dimensions of the noodle's cross-section, usually obtained through equivalent calculations. This parameter reflects the volume-to-surface area ratio of the noodles, thus affecting the efficiency of heat transfer from steam to the noodle's interior. The raw material requirement coefficient is a comprehensive indicator used to quantify the impact of the noodles' own physical properties (such as initial moisture content and equivalent cross-sectional diameter) on the difficulty of the cooking process. The larger the coefficient value, the more difficult the noodles are to cook.

[0033] The steam temperature, steam saturation, and steam velocity in the cooking zone are collected, and the heat source supply coefficient, which characterizes the heating capacity, is calculated. The cooking zone is the area in the noodle cooking machine used to heat the noodles with steam, causing a gelatinization reaction and setting the noodles. Within this zone, the noodles move via a conveyor belt and are exposed to a high-temperature, high-humidity steam environment. Steam temperature refers to the actual temperature of the steam in the cooking zone. This temperature is one of the key heat source parameters affecting the heat transfer rate and noodle cooking efficiency. Steam saturation refers to the degree of steam saturation in the cooking zone. Saturated steam has a higher latent heat, enabling it to transfer heat to the noodles more effectively, thus affecting the cooking effect. Steam velocity refers to the flow rate of the steam in the cooking zone. This velocity affects the uniformity of steam distribution within the cooking zone and the contact efficiency with the noodles, thereby affecting heat transfer. The heat source supply coefficient is a comprehensive indicator used to quantify the driving force of the external thermal environment (such as steam temperature, steam saturation, and steam velocity) on the cooking process. The larger the coefficient value, the stronger the heating capacity of the heat source.

[0034] The heat transfer efficiency coefficient, characterizing the efficiency of steam thermal energy utilization, is calculated by determining the current conveyor belt load and noodle arrangement density, and combining these with the current raw material demand coefficient and heat source supply coefficient. The conveyor belt load refers to the mass or quantity of noodles carried per unit area or unit length of the conveyor belt. An excessively high load will affect the steam's penetration ability and reduce heat transfer efficiency. The noodle arrangement density refers to the density of the noodles' distribution on the conveyor belt. An excessively high density will hinder sufficient contact between the steam and the noodles, leading to uneven cooking. The heat transfer efficiency coefficient is a comprehensive indicator used to characterize the impact of the combination of the current conveyor belt load and noodle arrangement density on the efficiency of steam thermal energy utilization under specific raw material and heat source conditions. A higher coefficient value indicates higher steam thermal energy utilization efficiency.

[0035] The degree of gelatinization and color difference of noodles were measured to calculate a quality feedback coefficient characterizing the actual cooked quality. The degree of gelatinization refers to the completeness of starch gelatinization after cooking. An ideal degree of gelatinization is a crucial indicator of noodle cooking quality. The color difference refers to the degree of color difference between the noodles before and after cooking, or between the noodles and a standard sample. This value is used to assess whether excessive oxidation or Maillard reactions occurred during the noodle cooking process, affecting the product's appearance. The quality feedback coefficient is a comprehensive indicator that directly reflects the final quality state of the noodles after cooking, including the degree of gelatinization and color difference. A higher coefficient indicates better cooked noodle quality.

[0036] A preset reference speed is invoked, and the target conveyor belt operating speed is calculated based on the reference speed, heat transfer efficiency coefficient, and the mass feedback coefficient. The current conveyor belt operating speed is then adjusted to the target conveyor belt operating speed. The reference speed refers to the preset operating speed of the noodle conveyor belt under ideal or standard operating conditions. This speed serves as the reference basis for calculating the target conveyor belt operating speed. The target conveyor belt operating speed refers to the conveyor belt operating speed that needs to be adjusted based on real-time calculation results to ensure that the noodles achieve optimal cooking quality. By adjusting to this speed, dynamic optimization control of the cooking process can be achieved.

[0037] In existing technologies, the conveyor speed of noodle cooking machines is usually fixed or adjusted based on manual experience. This crude control method often exhibits lag and insufficient precision when faced with multiple disturbances such as differences in the characteristics of the noodle raw materials, fluctuations in the heat source in the cooking zone, and changes in the load on the conveyor belt and the density of the noodles. For example, when a batch of noodles that are difficult to cook enters the cooking machine, or when the steam supply capacity decreases, existing technologies cannot adjust the conveyor speed in real time to extend the cooking time, resulting in uneven cooking of the noodles, or even problems such as undercooked or over-gelatinized noodles.

[0038] In contrast, the process in this embodiment constructs a comprehensive evaluation system for the maturation process by introducing multiple key indicators such as raw material demand coefficient, heat source supply coefficient, heat transfer efficiency coefficient, and quality feedback coefficient. In the example above, when the initial moisture content and diameter of the noodles are high, resulting in a high raw material demand coefficient, while insufficient steam supply leads to a low heat source supply coefficient, and dense belt load results in a low heat transfer efficiency coefficient, these real-time calculated coefficients can accurately reflect the true state of the current maturation environment. More importantly, this process can dynamically calculate and adjust the target conveyor belt speed based on these coefficients and the quality feedback coefficient after maturation. This real-time, coordinated control mechanism allows the conveyor belt speed to precisely adapt to constantly changing working conditions.

[0039] Specifically, when the system detects increased cooking difficulty, weakened heat supply, or reduced heat transfer efficiency, it can proactively reduce the conveyor belt speed, extending the noodle's residence time in the cooking zone to compensate for the adverse effects on the cooking result. Conversely, when conditions are favorable, the system can appropriately increase the speed to boost production capacity. This intelligent speed control strategy based on multi-dimensional parameters effectively solves the problems of control lag and insufficient precision in existing technologies, ensuring that noodles achieve ideal gelatinization uniformity and color consistency under different operating conditions. Therefore, the process in this embodiment not only improves the adaptability of the noodle cooking process and the stability of product quality but also provides an effective technical approach to achieving a balance between energy saving and production capacity.

[0040] This application further proposes the following process for calculating and obtaining the raw material demand coefficient:

[0041] Obtaining the initial moisture content and equivalent cross-sectional diameter of the noodles is crucial. The initial moisture content directly affects the rate of water absorption and expansion, as well as the cooking time, while the equivalent cross-sectional diameter determines the path length and resistance of heat transfer into the noodle's interior. These parameters are fundamental data for assessing the cooking difficulty of noodles. Acquisition methods include real-time measurement using online sensors, such as near-infrared spectroscopy to measure moisture content, or image analysis of the noodle cross-section using a machine vision system to calculate the equivalent diameter; alternatively, offline sampling and inspection can be used, such as manual sampling followed by drying to determine moisture content, or measuring the diameter using tools like calipers to calculate the equivalent diameter.

[0042] The difference between the current initial moisture content and the minimum initial moisture content is compared with the difference between the maximum initial moisture content and the minimum initial moisture content to obtain the initial moisture content index. This step aims to standardize the actually measured current initial moisture content into a dimensionless initial moisture content index, ensuring its value falls within a specific range to facilitate subsequent mathematical calculations and model integration. This ratio quantifies the relative position of the current moisture content within the entire permissible range, thus reflecting its impact on the cooking difficulty. The minimum and maximum initial moisture contents represent the permissible fluctuation range or historical extreme values ​​of the initial moisture content of noodles under a specific noodle production process or product standard. These parameters are typically pre-set and stored in the control system based on production experience, product specifications, or experimental data.

[0043] The equivalent diameter index is obtained by comparing the difference between the square of the noodle cross-section's equivalent diameter and the square of the minimum cross-section diameter with the difference between the square of the maximum cross-section diameter and the square of the minimum cross-section diameter. This process also employs normalization to convert the noodle's equivalent cross-section diameter into a dimensionless equivalent diameter index. Considering the area effect of heat transfer during noodle cooking, using the square of the diameter more accurately reflects the impact of cross-sectional dimensions on the cooking difficulty. The minimum and maximum cross-sectional diameters represent the allowable range of noodle cross-sectional dimensions. This ratio processing standardizes the cross-sectional dimension data of different noodle specifications, placing them between 0 and 1, facilitating unified modeling. This method ensures that the equivalent diameter index accurately reflects the relative position of the current cross-sectional dimension within the entire allowable range, providing standardized input for subsequent cooking difficulty assessment.

[0044] Substituting the initial moisture content index and the equivalent cross-sectional diameter index into the formula Obtain the raw material demand coefficient In the formula This means that the lower the moisture content, the harder the noodles are to cook, because noodles with low moisture content need to absorb more water to reach a cooked state; while This means that the larger the cross-sectional diameter, the harder it is for the noodles to cook because it takes longer for heat to transfer to the center of the noodles. By multiplying and taking the square root, this formula balances the effects of these two factors. , It is used to comprehensively reflect the influence of the physical properties of the noodles themselves before they enter the cooking zone on the cooking process. The higher the value, the more difficult it is to cook the noodles. This quantitative method provides a precise basis for adjusting subsequent cooking process parameters. Among these, The initial moisture content index. The equivalent diameter index of the cross section.

[0045] Through the above process, this application can transform the initial physical properties of noodles (moisture content and cross-sectional diameter) into a quantified and standardized raw material requirement coefficient. This coefficient, as a key input parameter in the noodle cooking process, accurately reflects the current cooking difficulty of the noodles, thus providing a precise basis for subsequent heat source supply, heat transfer efficiency assessment, and final conveyor belt speed adjustment. This allows the entire noodle conveying process to be dynamically adjusted according to the characteristics of the noodles themselves, avoiding undercooking or overcooking caused by differences in noodle characteristics, and significantly improving the intelligence and precision of the cooking process.

[0046] For example, suppose a noodle cooking machine needs to accurately assess the cooking difficulty of noodles during production. First, an online sensor obtains the current initial moisture content of the noodles in real time, which is 28%, and the equivalent diameter of the noodle cross-section is 2.0 mm. Second, the minimum initial moisture content of the noodles is set to 25%, and the maximum initial moisture content to 35%. The initial moisture content index is then calculated to be 0.3. Simultaneously, the minimum cross-sectional diameter of the noodles is set to 1.0 mm, and the maximum cross-sectional diameter to 3.0 mm. The equivalent diameter index is then calculated to be 0.375. Finally, the calculated initial moisture content index... 0.3 and the equivalent diameter index of the cross section Substitute 0.375 into the formula for calculating the raw material demand coefficient. The raw material requirement coefficient for noodles can be obtained through the above calculations. The value is 0.512. This coefficient will serve as an important basis for adjusting the conveyor belt speed during the subsequent noodle cooking process. For example, when... A higher value indicates that the noodles are harder to cook, and the conveyor belt speed may need to be reduced to extend the cooking time; conversely, when... A lower value indicates that the noodles cook more easily, and the conveyor belt speed can be appropriately increased to improve production efficiency.

[0047] Through the above technical solution, this application provides a scientific and quantitative method for calculating the raw material requirement coefficient. This method, by standardizing the initial moisture content and equivalent cross-sectional diameter of the noodles, and combining this with a specific mathematical model, can accurately assess the impact of the noodles' physical properties on the cooking process. This allows the noodle cooking machine to generate a precise raw material requirement coefficient based on the actual cooking difficulty of different batches and specifications of noodles. This coefficient, as a key input in the noodle conveying process, effectively guides the adjustment of subsequent cooking parameters, avoiding the uncertainty and errors caused by traditional experience-based judgments. This significantly improves the accuracy and stability of the noodle cooking process, contributing to the production of more uniform quality and better-tasting noodle products.

[0048] This application further proposes the following process for calculating the heat source supply coefficient:

[0049] The process involves acquiring the steam temperature, steam saturation, and steam flow rate in the cooking zone. The steam temperature refers to the actual temperature of the steam within the cooking chamber during the noodle cooking process. This can be achieved by deploying temperature sensors within the cooking zone, such as thermocouples or resistance temperature detectors (RTDs), to monitor and collect steam temperature data in real time. Steam saturation refers to the degree of steam saturation within the cooking zone, reflecting the content of liquid water droplets in the steam. Steam saturation can be obtained by directly measuring it with a humidity sensor, or by measuring the steam pressure and temperature and calculating it using a steam property table. Steam flow rate refers to the speed at which the steam flows within the cooking zone, reflecting the efficiency of heat transfer from the steam to the noodles. Steam flow rate can be acquired using flow meters installed in the steam pipes or cooking zone, such as vortex flow meters, differential pressure flow meters, or ultrasonic flow meters, for real-time monitoring.

[0050] The difference between the current steam temperature and the lowest steam temperature is compared with the difference between the highest steam temperature and the lowest steam temperature to obtain the steam temperature index. This step is a concrete implementation of the maximum-minimum normalization process, which aims to linearly map the original steam temperature value to a dimensionless index between 0 and 1, eliminating the influence of its original dimensions. This ratio processing can be implemented by writing a corresponding calculation module in a programmable logic controller (PLC) or distributed control system (DCS) to obtain the current temperature value in real time and perform the calculation, or by using a dedicated data processing unit or embedded system.

[0051] The steam saturation index is obtained by comparing the difference between the current steam saturation and the minimum saturation with the difference between the maximum and minimum saturation. This process also maps the original steam saturation value to a dimensionless exponent between 0 and 1, ensuring its weight in subsequent calculations remains consistent with other parameters. This calculation can be implemented in the control system using software algorithms, or efficiently computed using hardware acceleration units such as mathematical coprocessors or field-programmable gate arrays (FPGAs).

[0052] The steam velocity index is obtained by comparing the difference between the current steam velocity and the minimum steam velocity with the difference between the maximum and minimum steam velocity. This step converts the original steam velocity value into a dimensionless exponent between 0 and 1, allowing it to be effectively compared and calculated with other normalized parameters. This process can be achieved by collecting velocity data from sensors and having the central processing unit execute a normalization algorithm, or by using a preset lookup table or curve fitting method to convert the velocity value into the corresponding exponent.

[0053] The minimum steam temperature, maximum steam temperature, minimum saturation, maximum saturation, minimum steam flow rate, and maximum steam flow rate are the boundary values ​​used for maximum-minimum normalization. They define the effective range of each physical quantity in a specific application scenario. These boundary values ​​can be determined through statistical analysis of historical operating data, equipment design specifications, process requirements, or industry standard presets, or through experimental testing or expert experience.

[0054] The steam thermal intensity factor is obtained by geometrically averaging the steam temperature index and the steam saturation index. The specific calculation method is as follows: substitute the steam temperature index and the steam saturation index into the formula. Obtain the steam thermal intensity factor ,in, This refers to the steam temperature index. Steam saturation index; steam heat intensity factor It is a comprehensive indicator used to quantify the potential heating capacity of steam within the curing zone. This factor is calculated by combining the steam temperature index. and steam saturation index This reflects the thermal energy characteristics of steam in two dimensions: temperature and saturation. Its calculation method uses a geometric mean, aiming to balance the influence of these two factors on thermal intensity, ensuring... It can effectively characterize the inherent thermal energy quality of steam.

[0055] The heat source supply coefficient is obtained by geometrically averaging the steam heat intensity factor and the steam velocity index; specifically, the calculation method is as follows: substitute the steam heat intensity factor and the steam velocity index into the formula. Obtain the heat source supply coefficient This coefficient is in the steam heat intensity factor Based on this, the steam velocity index was further introduced. It takes into full account the inherent thermal energy quality of steam and its transfer efficiency in the curing zone. The calculation also uses the geometric mean to ensure the balanced effect of each factor; , Used to comprehensively reflect the driving force of the external thermal environment of the curing zone on the curing process. The higher the value, the stronger the heating capacity. For steam heat intensity factor, This is the steam velocity index.

[0056] This application's scheme achieves accurate quantification of the heating capacity of the external thermal environment of the curing zone through refined calculation of the heat source supply coefficient. First, raw data reflecting the current thermal environment state are obtained by real-time monitoring of three key physical parameters: steam temperature, steam saturation, and steam velocity in the curing zone. To eliminate differences between these data of different dimensions and enable effective comprehensive calculation, these raw data are subjected to max-min normalization, resulting in dimensionless steam temperature index, steam saturation index, and steam velocity index. Subsequently, the steam temperature index and steam saturation index are combined using a geometric mean to calculate the steam heat intensity factor. This factor effectively characterizes the potential thermal energy quality of steam itself. Based on this, the steam thermal intensity factor is further... With steam velocity index By performing a geometric mean, the heat source supply coefficient was finally obtained. .this The value comprehensively reflects the overall driving force of the steam temperature, saturation, and flow velocity in the cooking zone on the cooking process. Through this multi-dimensional, step-by-step calculation method, this scheme can provide a more accurate and comprehensive assessment of the heat source supply, thereby providing a reliable basis for adjusting the conveyor belt speed in the noodle cooking process and avoiding fluctuations in cooking results caused by inaccurate heat source assessment.

[0057] The following is a concrete example to illustrate this. Assume that during the noodle cooking process, the steam temperature range in the cooking zone is set to 100℃ to 120℃, the steam saturation range is set to 0.8 to 1.0, and the steam flow rate range is set to 50 kg / h to 100 kg / h. At a certain moment, the real-time monitored steam temperature is 110℃, the steam saturation is 0.9, and the steam flow rate is 75 kg / h. First, a maximum-minimum normalization process is performed to obtain the steam temperature index. The steam saturation index is 0.5. The value is 0.5. (Steam velocity index) The value is 0.5. Next, the steam heat intensity factor is calculated. Steam temperature index 0.5 and steam saturation index Substitute 0.5 into the formula ,get 0.5. Finally, calculate the heat source supply coefficient. : Steam heat intensity factor 0.5 and steam velocity index Substitute 0.5 into the formula ,get 0.5. Through the above calculations, the heat source supply coefficient under the current operating conditions can be obtained. The coefficient 0.5 will serve as an important input parameter for subsequent calculations of the target conveyor belt's operating speed.

[0058] Through the above technical solution, this application provides a more accurate and comprehensive method for calculating the heat source supply coefficient. This method fully considers multiple key factors such as the temperature, saturation, and flow rate of steam in the cooking zone, and through scientific normalization and multi-level factor calculation, comprehensively quantifies these complex thermal environment parameters into a single heat source supply coefficient. This allows the control system of the noodle cooking machine to more accurately assess the actual heating capacity of the cooking zone, thereby more precisely matching the cooking requirements of the noodles when calculating the target conveyor belt speed. This accurate assessment of heating capacity helps avoid uneven or overcooked cooking caused by heat source fluctuations or inaccurate assessments, significantly improving the quality stability of cooked noodles, reducing energy consumption in the production process, and increasing overall production efficiency.

[0059] This application further proposes the following process for calculating the heat transfer efficiency coefficient:

[0060] The raw material demand coefficient, heat source supply coefficient, conveyor belt load, and noodle arrangement density are obtained. The raw material demand coefficient and heat source supply coefficient are pre-calculated, reflecting the cooking difficulty of the noodles themselves and the heating capacity of the external environment of the cooking zone, respectively. These coefficients can be calculated from previous steps, such as collecting the initial moisture content and equivalent cross-sectional diameter of the noodles through sensors, as well as parameters such as steam temperature, steam saturation, and steam flow rate in the cooking zone, and then calculating them using corresponding mathematical models. The conveyor belt load refers to the total weight or volume of noodles carried on the conveyor belt of the cooking machine. This parameter can be obtained by using a weighing sensor installed under the conveyor belt or by estimating the noodle stack height through a vision recognition system. The noodle arrangement density refers to the density of the noodle distribution per unit area on the conveyor belt. This parameter can be obtained by analyzing the distribution image of the noodles on the conveyor belt through a machine vision system to calculate the noodle coverage or gap ratio.

[0061] Substitute the raw material demand coefficient and the heat source supply coefficient into the formula. Obtain the ripening difficulty factor Difficulty factor of ripening It is a dimensionless parameter used to comprehensively evaluate the relative ease or difficulty of cooking noodles under current raw material characteristics and heat source conditions. , A value close to 0 indicates that the food is very easy to cook (i.e., the demand for raw materials is small while the supply of heat source is large). A value close to 1 indicates extremely difficult cooking (i.e., high demand for raw materials but low supply of heat sources). This is the raw material demand coefficient. The heat source supply coefficient; These are extremely small positive numbers, used to avoid cases where the denominator is zero and to ensure the stability of the calculation;

[0062] Based on the ripening difficulty factor, the optimal load under the current working condition is determined by linear interpolation within the allowable load range of the conveyor belt, and the optimal arrangement density under the current working condition is determined by linear interpolation within the allowable arrangement density range of the noodles; wherein, the larger the ripening difficulty factor, the closer the optimal load and the optimal arrangement density are to the lower limit of their allowable range; the specific calculation method is as follows:

[0063] Substitute the ripening difficulty factor into the formula Obtain the optimal load under the current operating conditions. Optimal load under current operating conditions Based on the current ripening difficulty factor η, the minimum allowable load of the conveyor belt is... and the maximum allowable load of the mesh belt An ideal load value is dynamically adjusted between these values. It reflects the ideal amount of noodles the conveyor belt should carry under the current level of noodle cooking difficulty, in order to maximize the utilization efficiency of steam heat energy. This is the minimum allowable load for the mesh belt. This is the maximum allowable load of the mesh belt. The degree of difficulty in maturation; minimum allowable load of the conveyor belt. and the maximum allowable load of the conveyor belt The operating range of the conveyor belt load is defined and used as the optimal load for calculating the current operating conditions. The boundary conditions. These parameters can be set through engineering calculations or experience based on factors such as the design specifications of the noodle cooking machine, the material of the conveyor belt, the driving capacity, and the space limitations of the cooking zone, or they can be determined through actual production testing.

[0064] Substitute the ripening difficulty factor into the formula Obtain the optimal perforation density under the current working conditions. Optimal arrangement density under current operating conditions Based on the current cooking difficulty factor η, at the minimum noodle arrangement density and the maximum density of noodles An ideal arrangement value, dynamically adjusted between these values, reflects the ideal distribution density of noodles on the conveyor belt under the current level of noodle cooking difficulty, ensuring that steam can penetrate the noodle layer evenly and effectively. For the minimum noodle density, For the maximum noodle density, The cooking difficulty factor; minimum noodle density. and the maximum density of noodles The operating range of noodle arrangement density is defined and used as the basis for calculating the optimal arrangement density under the current working conditions. The boundary conditions. These parameters can be determined through theoretical analysis or experimental verification based on factors such as the type, shape, cooking characteristics, and steam penetration ability of the noodles. Alternatively, they can be set by observing the cooking effect of noodles at different arrangement densities and combining image recognition technology to perform quantitative analysis of noodle distribution.

[0065] The load deviation index is obtained by comparing the difference between the current load and the optimal load under the current operating conditions with the difference between the maximum allowable load and the minimum allowable load of the conveyor belt. The load deviation index quantifies the degree of deviation between the current load and the optimal load under the current operating conditions and normalizes it to the total range of allowable load of the conveyor belt.

[0066] The density deviation index is obtained by comparing the difference between the current noodle arrangement density and the optimal arrangement density under the current working conditions with the difference between the maximum noodle arrangement density and the minimum noodle arrangement density. The density deviation index quantifies the degree of deviation between the current noodle arrangement density and the optimal arrangement density under the current working conditions, and normalizes it to the total range of allowable noodle arrangement densities.

[0067] Substitute the load deviation index and density deviation index into the formula Obtain the heat transfer efficiency coefficient Heat transfer efficiency coefficient This is a key parameter used to quantify the combined impact of the current load on the conveyor belt and the noodle arrangement density on steam thermal energy utilization efficiency under given raw material and heat source conditions. This coefficient is calculated using an exponential decay function, which includes the load deviation index (…). ) and density deviation index ( The square of the term. ,when When, it indicates that neither the actual load nor the arrangement density exceeds the ideal value under the current operating conditions. When the load or packing density far exceeds the ideal value, steam can hardly penetrate, and heat transfer is severely hindered. The calculation of this coefficient accurately reflects the constraint of physical layout on the thermal efficiency of the curing process. Among these factors, The load deviation index. This is the density deviation index.

[0068] The solution in this application introduces an effect on the heat transfer efficiency coefficient. The refined calculations solved the problem of unstable steam heat energy utilization efficiency caused by changes in conveyor belt load and noodle arrangement density during noodle cooking. This scheme first obtains the raw material requirement coefficient for the noodles. Heat supply coefficient Key parameters include conveyor belt load and noodle arrangement density. Based on raw material demand coefficients... and heat supply coefficient The system calculates the ripening difficulty factor. This factor comprehensively reflects the balance between the intrinsic requirements for noodle cooking and the external heat supply. Subsequently, it is combined with a cooking difficulty factor. Based on the current conveyor belt load and noodle arrangement density, the system further determines the load deviation index. and density deviation index These two indices quantify the difference between the current physical distribution of noodles on the conveyor belt and the ideal distribution, directly affecting whether steam can effectively penetrate the noodle layer and transfer heat. Finally, these deviation indices are substituted into a specific exponential decay formula to accurately calculate the heat transfer efficiency coefficient. Heat transfer efficiency coefficient The introduction of this technology allows the noodle conveying process to more accurately assess the actual heat transfer efficiency under the current cooking environment, thus providing a more reliable basis for calculating the subsequent target conveyor belt operating speed. In this way, the noodle cooking machine can dynamically adjust the conveyor speed according to actual working conditions, ensuring that noodles achieve uniform and efficient cooking results under different loads and densities, avoiding uneven cooking or energy waste caused by obstructed heat transfer.

[0069] As a specific implementation method, the heat transfer efficiency coefficient The calculation can be performed as follows: First, obtain the raw material requirement coefficient for the current batch of noodles through online sensors or a preset model. and the heat supply coefficient within the maturation zone For example, the raw material demand coefficient. It can be calculated using the initial moisture content and equivalent cross-sectional diameter of the noodles, while the heat source supply coefficient... This can be calculated using steam temperature, steam saturation, and steam flow rate. Simultaneously, a vision recognition system installed above the conveyor belt monitors the noodle distribution in real time, thereby obtaining the current conveyor belt load and noodle density. Next, the obtained raw material requirement coefficient will be... and heat supply coefficient Substitute into the formula The ripening difficulty factor was calculated. For example, if the noodles are difficult to cook ( High) and weak heat source supply ( (Low), then the difficulty factor for maturation. It will be relatively high. Then, based on this ripening difficulty factor... By combining preset minimum and maximum allowable loads of the conveyor belt, minimum noodle arrangement density, and maximum noodle arrangement density, the system can determine the optimal load and optimal arrangement density under the current operating conditions. For example, when the cooking difficulty factor... At higher loads, to ensure efficient heat transfer, the system may tend to recommend lower loads and sparser packing densities. Subsequently, the current actual belt load is compared with the calculated optimal load, and normalization is performed to obtain the load deviation index. Similarly, the current noodle arrangement density is compared with the calculated optimal arrangement density and normalized to obtain the density deviation index. Finally, the calculated load deviation index... and density deviation index Substitute into the formula The current heat transfer efficiency coefficient can then be obtained. For example, if both the actual load and the arrangement density are close to or better than the optimal values, then and The coefficient of performance may be negative or zero, resulting in a heat transfer efficiency coefficient. Approaching 1. Conversely, if the load is too heavy or the arrangement is too dense, it leads to... or If the value is large and positive, then the heat transfer efficiency coefficient is high. It will decrease significantly, reflecting a reduction in heat transfer efficiency.

[0070] Through the above technical solution, the noodle conveying process of the noodle cooking machine can more accurately assess and quantify the actual utilization efficiency of steam heat energy during the noodle cooking process. In the traditional noodle cooking process, the noodle load and arrangement density on the conveyor belt are often key factors affecting heat transfer, but their impact is difficult to accurately capture and quantify. This results in the conveyor belt speed adjustment not being able to fully adapt to the actual working conditions, leading to uneven cooking, energy waste, or product quality fluctuations. This application introduces a cooking difficulty factor, load deviation index, and density deviation index, combined with a specific mathematical model, to dynamically and accurately calculate the heat transfer efficiency coefficient. This coefficient directly reflects the degree of constraint of the current physical layout on heat transfer, enabling the noodle conveying process to fully consider and compensate for the heat efficiency loss caused by changes in conveyor belt load and noodle arrangement density when calculating the target conveyor belt operating speed. Therefore, this solution can significantly improve the precise control capability of the cooking process, ensuring that noodles achieve uniform and efficient cooking results under various working conditions, thereby improving product quality consistency and optimizing the utilization efficiency of steam heat energy.

[0071] This application further proposes the following process for calculating and obtaining the quality feedback coefficient:

[0072] Obtain the degree of gelatinization and color difference values. The degree of gelatinization is a quantitative indicator of the extent of starch gelatinization during noodle cooking. It can be measured using various methods. For example, differential scanning calorimetry (DSC) can be used to measure the onset temperature, peak temperature, and enthalpy change of the starch gelatinization endothermic peak to assess the degree of gelatinization. Alternatively, a rapid viscosity analyzer (RVA) can be used to measure the viscosity change curve of the noodle sample during heating and cooling, indirectly reflecting the degree of gelatinization through parameters such as peak viscosity and final viscosity. The color difference value is a quantitative indicator of the color change of noodles before and after cooking, reflecting the degree of browning or color uniformity. It can be measured using a colorimeter. For example, the L* (brightness), a* (red-green hue), and b* (yellow-blue hue) values ​​of the noodle surface can be measured, and then the total color difference ΔE* can be calculated using the formula.

[0073] Substitute the current degree of gelatinization into the formula Obtain the gelatinization index This step aims to convert the actual measured degree of gelatinization. Convert to an exponent between 0 and 1 This index can quantify the current degree of gelatinization and the target degree of gelatinization. The degree of closeness. When The closer hour, The closer to 1, the more ideal the gelatinization degree; when Deviation The more, The closer the value is to 0, the less ideal the gelatinization. The exponent is calculated using a Gaussian function, meaning that the further the value deviates from the target, the faster the exponent decreases, and the standard deviation... The steepness of the descent, i.e., the tolerance, was controlled. Among these, The current degree of gelatinization, The target gelatinization degree is the preset ideal degree of noodle cooking and is a key quality parameter for producing high-quality noodles. This value is usually set according to product standards, consumer preferences, or experience. The standard deviation is used to control the tolerance in the calculation of the gelatinization index, that is, the degree to which the current gelatinization degree deviates from the target gelatinization degree. A larger standard deviation indicates a higher tolerance for deviations in gelatinization degree, while a smaller standard deviation indicates a lower tolerance and requires the gelatinization degree to be closer to the target value.

[0074] The current color difference value is compared with the maximum permissible color difference, and then the ratio is limited to an upper limit of 1 using a min function to obtain the color difference index. This step aims to convert the actual measured color difference value into an index between 0 and 1. This index quantifies the relative relationship between the current color difference value and the maximum permissible color difference. The ratio processing ensures that the index changes linearly with the color difference value when it is within the maximum permissible range. A min function limits the ratio to an upper limit of 1, ensuring that when the color difference value exceeds the maximum permissible color difference, the index remains 1, indicating that the color difference has reached or exceeded an unacceptable level. The maximum permissible color difference is the upper limit of acceptable color change after the noodles are cooked; exceeding this value indicates that the noodle color is unacceptable. This value is typically set based on product standards or visual sensory evaluation.

[0075] Substitute into the formula Obtain the quality feedback coefficient , , This directly reflects the final quality of the noodles after cooking. This indicates extremely poor quality (severe deviation in gelatinization degree or extreme color difference). This indicates perfect quality, among which, The degree of gelatinization index. This is the color difference index.

[0076] This application's solution first precisely quantifies the deviation between the current degree of gelatinization and the target degree of gelatinization in the noodles, and then calculates a gelatinization index based on the standard deviation, thereby reflecting the quality of gelatinization. Simultaneously, by ratioing the current color difference value to the maximum permissible color difference and limiting the range, a color difference index is obtained to quantify the acceptable degree of color variation in the noodles. Subsequently, these two independent indices are substituted into a specific nonlinear combination formula to calculate the final quality feedback coefficient. The design of this combined formula makes the gelatinization index... and color difference index These factors can influence each other and jointly determine the final quality evaluation. For example, even if the degree of gelatinization is ideal, if the color difference is too large, the quality feedback coefficient will be affected. It will also decrease significantly; conversely, it will increase. This non-linear combination ensures the quality feedback coefficient. It can comprehensively and sensitively reflect the actual cooking quality of noodles, avoiding the one-sidedness of a single indicator. This precisely calculated quality feedback coefficient This speed is then used in conjunction with the baseline speed and heat transfer efficiency coefficient to calculate the target conveyor belt speed. In this way, the noodle cooking machine can intelligently adjust the conveyor belt speed in real time according to the actual cooking quality of the noodles, forming a closed-loop control system that continuously optimizes the cooking effect and ensures the stability and consistency of product quality.

[0077] The following is a concrete example to illustrate this. Suppose that during a noodle cooking process, the current degree of gelatinization is measured using a rapid viscosity analyzer. The color difference is 85%, and the current color difference value measured by the colorimeter is 3.5. The preset target degree of gelatinization is... The standard deviation is set at 90%. The maximum permissible color difference is set to 5%, with a set limit of 5. First, the current degree of gelatinization (85%), the target degree of gelatinization (90%), and the standard deviation (5%) are substituted into the formula for calculating the degree of gelatinization index to obtain the degree of gelatinization index. The value is approximately 0.6065. Next, the current color difference value of 3.5 is compared to the maximum permissible color difference of 5, resulting in a ratio of 0.7. This ratio is then capped at 1 using a min function to obtain the color difference index. The value is 0.7. Finally, the gelatinization index is... (0.6065) and color difference index (0.7) Substitute into the formula for calculating the quality feedback coefficient The quality feedback coefficient was calculated. It is approximately 0.4014. This calculated quality feedback coefficient This will serve as an important basis for subsequent calculations of the target conveyor belt's operating speed, guiding the optimization and adjustment of the noodle cooking process.

[0078] Through the above technical solution, this application can provide a highly accurate and sensitive evaluation of the cooked quality. This evaluation can more realistically reflect the actual cooked state of the noodles, thereby enabling more precise and timely adjustment of the conveyor belt speed of the noodle cooking machine. This effectively avoids undercooking or overcooking caused by inaccurate quality feedback, significantly improves the uniformity and consistency of noodle cooking, and ultimately ensures that the noodle products meet the expected taste, color, and quality standards, reducing the scrap rate and improving production efficiency.

[0079] This application further proposes the following process for calculating and obtaining the target conveyor belt operating speed:

[0080] The system acquires the reference speed, heat transfer efficiency coefficient, and quality feedback coefficient. The reference speed refers to the speed at which the conveyor belt of the noodle cooking machine operates under ideal or standard conditions. This speed can be preset to a fixed value, for example, based on different noodle types, equipment models, or production experience; alternatively, it can be dynamically adjusted based on historical production data, expert experience systems, or preset production plans to adapt to different production batches or product requirements. The heat transfer efficiency coefficient is an indicator characterizing the efficiency of steam thermal energy utilization. It reflects the degree to which the conveyor belt load and noodle arrangement density affect the heat transfer effect during noodle cooking under given raw material and heat source conditions. This coefficient can be calculated by real-time monitoring of the conveyor belt load and noodle arrangement density using sensors, combined with a preset heat transfer efficiency model; alternatively, it can be calculated by analyzing the noodle arrangement density using image recognition technology, combined with the conveyor belt load obtained by weighing sensors, and then substituting these data into a preset heat transfer efficiency model. The quality feedback coefficient directly reflects the final quality state of the noodles after cooking, comprehensively considering the degree of gelatinization and color difference. This coefficient can be calculated by detecting the degree of gelatinization of noodles online or offline (e.g., using a starch gelatinization meter) and color difference value (e.g., using a colorimeter), and then calculating it according to a preset quality assessment model; alternatively, it can also be calculated by using near-infrared spectroscopy or a machine vision system to monitor the degree of cooking and color change of noodles in real time, and converting them into a gelatinization index and a color difference index, thereby calculating the quality feedback coefficient.

[0081] The target conveyor belt operating speed is obtained by multiplying the reference speed by the heat transfer efficiency coefficient and the mass feedback coefficient; wherein, the heat transfer efficiency coefficient and the mass feedback coefficient together constitute a synergistic correction factor for the reference speed; the specific calculation method is as follows: substituting the reference speed, heat transfer efficiency coefficient, and mass feedback coefficient into the formula. Obtain the running speed of the target conveyor belt ,in, As the reference speed, The heat transfer efficiency coefficient, This refers to the quality feedback coefficient. This step aims to accurately calculate the target conveyor belt speed. The calculation logic can be implemented using the built-in algorithm module of the controller (e.g., a programmable logic controller (PLC) or industrial control computer), receiving the input coefficients, performing multiplication operations, and outputting the target speed; alternatively, the calculation logic can be implemented in the host computer software, communicating with the lower-level controller via a data interface to send the calculation results to the controller for adjusting the conveyor belt speed. Obtaining the target conveyor belt speed. The purpose is to calculate a dynamically adjustable conveyor belt speed based on the above formula, which can adapt to the current production conditions and quality requirements. The calculation unit can directly output the calculation results to the frequency converter or servo drive to control the conveyor belt motor; or, the calculation results can be used as the setpoint of the control system, and closed-loop control can be performed through proportional-integral-derivative (PID) or other control algorithms to make the actual conveyor belt speed approximate the target conveyor belt speed.

[0082] The solution in this application introduces a multiplicative model to adjust the base speed. Heat transfer efficiency coefficient and quality feedback coefficient These are organically combined to dynamically determine the target conveyor belt speed. Specifically, the reference speed The basic operating speed of the conveyor belt under ideal conditions was set. Heat transfer efficiency coefficient. This serves as the first correction factor for the baseline speed, reflecting the impact of the current belt load and noodle arrangement density on steam thermal energy utilization efficiency. When the heat transfer efficiency is low (i.e.... A lower heat transfer value indicates insufficient heat absorption by the noodles, requiring a reduction in conveyor belt speed to extend the cooking time; conversely, a higher heat transfer value indicates higher heat transfer efficiency. (If the value is relatively large), the conveyor belt speed can be appropriately increased. Quality feedback coefficient. As the second correction factor, it directly reflects the actual cooking quality of the noodles. When the cooking quality of the noodles is poor (i.e., When the value is small, the conveyor belt speed needs to be reduced to improve the curing effect; when the curing quality is good (i.e., (If the value is relatively large), the conveyor belt speed can be maintained or increased. By multiplying these three parameters, this scheme ensures the target conveyor belt operating speed. It can simultaneously balance production efficiency and product quality, achieving refined and adaptive control of the cooking process. This integrated computing approach enables the system to intelligently adjust the conveyor belt speed based on real-time feedback from raw material characteristics, heat source supply, conveyor belt load, and final product quality, thereby optimizing the entire noodle cooking process.

[0083] The following is a specific example to illustrate this. As a specific implementation method, assume the noodle cooking machine operates at its base speed under standard conditions. The speed is set to 10 meters per minute. At a certain moment, the system detects the impact of the current mesh belt load and noodle arrangement density on the heat transfer efficiency coefficient. A calculated value of 0.8 may indicate that the noodles were piled up too densely or had a slightly higher load, leading to a decrease in heat transfer efficiency. Simultaneously, a quality feedback coefficient was calculated by detecting the gelatinization degree and color difference values ​​of the cooked noodles. A value of 0.9 may indicate that the noodles are slightly undercooked or have a slightly off-color. Substituting these values ​​into the formula... The calculated speed is 7.2 meters per minute. At this point, the control system will adjust the speed according to the calculated target conveyor belt speed. The current operating speed of the conveyor belt can be adjusted to 7.2 meters per minute. For example, by sending a command to the servo motor driver, the actual operating speed of the conveyor belt can be adjusted from a previous value to 7.2 meters per minute to extend the residence time of the noodles in the cooking zone, thereby compensating for the decrease in heat transfer efficiency and improving the cooking quality of the noodles.

[0084] Through the above technical solution, this application provides a precise and adaptive method for calculating the target conveyor belt operating speed. This method effectively solves the problem of how to uniformly and dynamically adjust the conveyor belt operating speed to optimize the cooking process in complex and ever-changing production environments by multiplicatively combining the reference speed with the heat transfer efficiency coefficient and the mass feedback coefficient. This enables the noodle cooking machine to intelligently adjust the cooking time according to real-time changes in raw material characteristics, heat source supply, conveyor belt load, and final product quality. This ensures noodle cooking quality while maximizing the utilization efficiency of steam heat energy, reducing energy waste, and minimizing product loss due to uneven or over-cooking.

[0085] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A noodle conveying process for a noodle cooking machine, characterized in that, Includes the following steps: The initial moisture content and equivalent cross-sectional diameter of the noodles were obtained, and the raw material requirement coefficient, which characterizes the difficulty of cooking the noodles, was calculated. The steam temperature, steam saturation, and steam velocity in the maturation zone were collected, and the heat source supply coefficient, which characterizes the heating capacity, was calculated. Determine the current mesh belt load and noodle arrangement density, and calculate the heat transfer efficiency coefficient, which characterizes the efficiency of steam thermal energy utilization, by combining the current raw material demand coefficient and heat source supply coefficient. The degree of gelatinization and color difference of the noodles were detected, and the quality feedback coefficient, which characterizes the actual cooked quality, was calculated. Call the preset reference speed, and calculate the target conveyor belt running speed based on the reference speed, heat transfer efficiency coefficient and the mass feedback coefficient, and adjust the current conveyor belt running speed to the target conveyor belt running speed; The calculation process for the raw material demand coefficient is as follows: Obtain the initial moisture content of the noodles and the equivalent diameter of the noodle cross-section; The initial moisture content index is obtained by comparing the difference between the current initial moisture content and the minimum initial moisture content with the difference between the maximum initial moisture content and the minimum initial moisture content. The equivalent diameter index is obtained by comparing the difference between the square of the equivalent diameter of the noodle cross section and the square of the minimum cross section diameter with the difference between the square of the maximum cross section diameter and the square of the minimum cross section diameter. Substituting the initial moisture content index and the equivalent cross-sectional diameter index into the formula Obtain the raw material demand coefficient ,in, The initial moisture content index. The equivalent diameter exponent of the cross section; The calculation process for the heat source supply coefficient is as follows: Obtain the steam temperature, steam saturation, and steam flow rate in the maturation zone; The steam temperature, steam saturation, and steam velocity in the maturation zone were all subjected to maximum and minimum normalization to obtain the steam temperature index, steam saturation index, and steam velocity index. The steam thermal intensity factor is obtained by geometrically averaging the steam temperature index and the steam saturation index. The heat source supply coefficient is obtained by geometrically averaging the steam heat intensity factor and the steam velocity index.

2. The noodle conveying process of the noodle cooking machine according to claim 1, characterized in that, The steam temperature, steam saturation, and steam flow rate in the maturation zone are normalized in the following ways: The steam temperature index is obtained by comparing the difference between the current steam temperature and the lowest steam temperature with the difference between the highest steam temperature and the lowest steam temperature. The steam saturation index is obtained by comparing the difference between the current steam saturation and the minimum saturation with the difference between the maximum saturation and the minimum saturation. The steam velocity index is obtained by comparing the difference between the current steam velocity and the minimum steam velocity with the difference between the maximum steam velocity and the minimum steam velocity.

3. The noodle conveying process of the noodle cooking machine according to claim 1, characterized in that, The calculation process for the heat transfer efficiency coefficient is as follows: Obtain the raw material demand coefficient, heat source supply coefficient, conveyor belt load, and noodle arrangement density; Substitute the raw material demand coefficient and the heat source supply coefficient into the formula. Obtain the ripening difficulty factor ,in, This is the raw material demand coefficient. For heat source supply coefficient, It is a very small positive number; Based on the cooking difficulty factor, conveyor belt load, and noodle arrangement density, the load deviation index and density deviation index are determined. Substitute the load deviation index and density deviation index into the formula Obtain the heat transfer efficiency coefficient ,in, The load deviation index. This is the density deviation index.

4. The noodle conveying process of the noodle cooking machine according to claim 3, characterized in that, The calculation process for the load deviation index and density deviation index is as follows: Based on the ripening difficulty factor, the optimal load under the current working condition is determined by linear interpolation within the allowable load range of the conveyor belt, and the optimal arrangement density under the current working condition is determined by linear interpolation within the allowable arrangement density range of the noodles; The greater the ripening difficulty factor, the closer the optimal load and the optimal arrangement density are to the lower limit of their allowable range. The load deviation index is obtained by comparing the difference between the current load of the conveyor belt and the optimal load under the current operating conditions with the difference between the maximum allowable load of the conveyor belt and the minimum allowable load of the conveyor belt. The density deviation index is obtained by comparing the difference between the current noodle arrangement density and the optimal arrangement density under the current working conditions with the difference between the maximum noodle arrangement density and the minimum noodle arrangement density.

5. The noodle conveying process of the noodle cooking machine according to claim 1, characterized in that, The process for calculating and obtaining the quality feedback coefficient is as follows: Obtain the degree of gelatinization and color difference values; Substitute the current degree of gelatinization into the formula. Obtain the gelatinization index ,in, The current degree of gelatinization, For the target degree of gelatinization, Standard deviation Used to control tolerance; The color difference index is obtained by comparing the current color difference value with the maximum allowable color difference and limiting the ratio to 1. Gelatinization index Substitute the color difference index into the formula Obtain the quality feedback coefficient ,in, The degree of gelatinization index. This is the color difference index.

6. The noodle conveying process of the noodle cooking machine according to claim 1, characterized in that, The calculation and acquisition process for the target conveyor belt speed is as follows: Obtain the reference velocity, heat transfer efficiency coefficient, and mass feedback coefficient; The target conveyor belt running speed is obtained by multiplying the reference speed with the heat transfer efficiency coefficient and the mass feedback coefficient. The heat transfer efficiency coefficient and the mass feedback coefficient together constitute a synergistic correction factor for the reference velocity.

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